Browsing by Author "Rocha, Orlando"
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- Comparison of adsorption equilibrium of fructose, glucose and sucrose on potassium gel-type and macroporous sodium ion-exchange resinsPublication . Nobre, Clarisse; Santos, M.J.; Dominguez, Ana; Torres, Duarte; Rocha, Orlando; Peres, António M.; Rocha, Isabel; Ferreira, Eugénio C.; Teixeira, José; Rodrigues, Lígia R.Adsorption 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 ºC, and a synergetic effect at 40 ºC. 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 25ºC.
- UV spectrophotometry method for the monitoring of galacto-oligosaccharides productionPublication . Dias, L.G.; Veloso, Ana C.A.; Correia, Daniela M.; Rocha, Orlando; Torres, Duarte; Rocha, Isabel; Rodrigues, Lígia R.; Peres, António M.Monitoring 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-1 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.