a b s t r a c tAdsorption equilibrium of fructose, glucose and sucrose was evaluated on sulfonated poly(styrene-codivinylbenzene) cation-exchange resins. Two types of resins were used: potassium (K + ) gel-type and sodium (Na + ) macroporous resins. Influence of the cation and effect of the resin structure on adsorption were studied. The adsorption isotherms were determined by the static method in batch mode for monocomponent and multi-component sugar mixtures, at 25 and 40• C, in a range of concentrations between 5 and 250 g L −1 . All adsorption isotherms were fitted by a linear model in this range of concentrations. Sugars were adsorbed in both resins by the following order: fructose > glucose > sucrose. Sucrose was more adsorbed in the Na + macroporous resin, glucose was identically adsorbed, and fructose was more adsorbed in the K + gel-type resin. Data obtained from the adsorption of multi-component mixtures as compared to the mono-component ones showed a competitive effect on the adsorption at 25• C, and a synergetic effect at 40• C. The temperature increase conducted to a decrease on the adsorption capacity for mono-component sugar mixtures, and to an increase for the multi-component mixtures. Based on the selectivity results, K + gel-type resin seems to be the best choice for the separation of fructose, glucose and sucrose, at 25• C.

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.

In this paper a detailed mathematical model is presented for the fermentative production of fructo-oligosaccharides with Aspergillus sp. The model accounts for hydrolysis and transfructolization reactions, as well as biomass formation and it contains 27 parameters that were determined from experimental data using a System Biology toolbox with the Simulated Annealing method for curve fitting. Several additional experiments were performed in bioreactors where the time variation of 7 state variables (Sucrose, Glucose, Fructose, 1-Kestose, Nystose, 1-fructosyl nystose and Biomass) was measured. Experimental data were compared with results from simulations using the estimated parameters and it was verified that the model can predict the FOS production profile. The good agreement between simulated and experimental data was verified by calculating the relative percentage deviation modulus, which was lower than 10% for all cases except one. The derived and validated model can be used for process optimization, for example for indicating which fed-batch strategy could be used to improve the production of FOS while minimizing glucose concentration.

a b s t r a c tMonitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple, fast and inexpensive UV spectrophotometric method, together with partial least squares regression (PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS) and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were trained and their prediction abilities evaluated by cross-validation and external validation being the results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra data of the fermentation samples, gave, in general, the best performance being able to accurately and precisely predict lactose and total GOS levels, with standard error of prediction lower than 13 g kg À1 and coefficient of determination for the external validation set of 0.93-0.94, showing residual predictive deviations higher than five, whereas lower precision was obtained with the multiple model generated with PLS. The results obtained show that UV spectrophotometry allowed an accurate and non-destructive determination of sugars in fermentation samples and could be used as a fast alternative method for monitoring GOS production.

It is known that the barley kernel size is an important factor regarding the uniformity of the malting and brewery processes, barley valuation, approval and market value. In order to facilitate the barley purchasing process, a fast field technique for kernel size evaluation, such as the image analysis technique proposed in this work, would be greatly appreciated as a fast and simple procedure for barley selection. In this study a close correlation between the image analysis and the standard EBC was obtained with a correlation factor of 0.999 and a regression coefficient of 0.991 between the two methodologies. The proposed IA methodology was found to accurately predict the Scarlett and Prestige barley varieties weight distribution especially when considering the crucial 'business transactions selection' classes.

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Metabolic engineering (ME) strategies have been implemented over the last few years, in order to improve microbial strains of interest in industrial biotechnology. With the advent of experimental data concerning to regulatory aspects, several efforts have been conducted to incorporate this information in genome-scale metabolic models, aiming at the improvement of phenotype simulation methods. How ever, most of these methods can be used only by computer science experts, since they are not available in user-friendly software ME frameworks. This work presents Reg40ptFlux, a computational framework for ME, that integrates methods for phenotype simulation and optimization strain design, relying on integrated metabolic and regulatory models. Meta-heuristic approaches such as Evolutionary Algorithms and Simulated Annealing were appropriately modified to accommodate the optimization tasks, and were applied to study the optimization of ethanol and succinic acid production using an integrated model of the E.coli host. The framework was implemented as a plug-in for OptFlux, an open-source software for ME, and it is available in the OptFlux web site (www.optftux.org).