Synthesis of isomaltooligosaccharides and oligodextrans in a recycle membrane bioreactor by the combined use of dextransucrase and dextranase

A recycle ultrafiltration membrane reactor was used to develop a continuous synthesis process for the production of isomaltooligosaccharides (IMO) from sucrose, using the enzymes dextransucrase and dextranase. A variety of membranes were tested and the parameters affecting reactor stability, productivity, and product molecular weight distribution were investigated. Enzyme inactivation in the reactor was reduced with the use of a non-ionic surfactant but its use had severe adverse effects on the membrane pore size and porosity. During continuous isomaltooligosaccharide synthesis, dextransucrase inactivation was shown to occur as a result of the dextranase activity and it was dependent mainly on the substrate availability in the reactor and the hydrolytic activity of dextranase. Substrate and dextranase concentrations (50-200 mg/mL(-1) and 10-30 U/mL(-1), respectively) affected permeate fluxes, reactor productivity, and product average molecular weight. The oligodextrans and isomaltooligosaccharides formed had molecular weights lower than in batch synthesis reactions but they largely consisted of oligosaccharides with a degree of polymerization (DP) greater than 5, depending on the synthesis conditions. No significant rejection of the sugars formed was shown by the membranes and permeate flux was dependent on tangential flow velocity. (C) 2004 Wiley Periodicals, Inc.

Flavor characterization of sugar-added pennywort (Centella asiatica L.) juices treated with ultra-high pressure and thermal processes

The flavor characteristics of pennywort juices with added sugar treated by ultra-high pressure, pasteurization, and sterilization were investigated using solid phase microextraction combined with gas chromatography-mass spectrometry. It was found that sesquiterpene hydrocarbons comprised the major class of volatile components present and the juices had a characteristic aroma due to the presence of volatiles including beta-caryophyllene and humulene and alpha-copaene. In comparison with heated juices, HPP-treated samples could retain more volatile compounds such as linalool and geraniol similar to those present in fresh juice, whereas some volatiles such as alpha-terpinene and ketone class were apparently formed by thermal treatment. All processing operations produced juice that was not significantly different in the concentration of total volatiles. Practical Application: Pennywort juice is considered a nutraceutical drink for health benefits. Therefore, to preserve all aroma and active components in this juice, a nonthermal process such as ultra-high pressure should be a more appropriate technique for retention of its nutritive values than pasteurization and sterilization.

Memory operations in rapid serial visual presentation

Short-term memory (STM) has often been considered to be a central resource in cognition. This study addresses its role in rapid serial visual presentation (RSVP) tasks tapping into temporal attention-the attentional blink (AB). Various STM operations are tested for their impact on performance and, in particular, on the AB. Memory tasks were found to exert considerable impact on general performance but the size of the AB was more or less immune to manipulations of STM load. Likewise, the AB was unaffected by manipulating the match between items held in STM and targets or temporally close distractors in the RSVP stream. The emerging picture is that STM resources, or their lack, play no role in the AB. Alternative accounts assuming serial consolidation, selection for action, and distractor-induced task-set interference are discussed.

A combined Bayesian Markovian approach for behaviour recognition

Numerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and Hidden Markov Models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.

A swarm pattern transformation method based on macroscopic parameter operations

The work reported in this paper is motivated by biomimetic inspiration - the transformation of patterns. The major issue addressed is the development of feasible methods for transformation based on a macroscopic tool. The general requirement for the feasibility of the transformation method is determined by classifying pattern formation approaches an their characteristics. A formal definition for pattern transformation is provided and four special cases namely, elementary and geometric transformation based on repositioning all and some robotic agents are introduced. A feasible method for transforming patterns geometrically, based on the macroscopic parameter operation of a swarm is considered. The transformation method is applied to a swarm model which lends itself to the transformation technique. Simulation studies are developed to validate the feasibility of the approach, and do indeed confirm the approach.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.