The aim of the present work was to study the potential of ultrafiltration with three polyacrylonitrile membranes (1, 10, and 25 kDa) to concentrate polyphenolic antioxidants in apple juice and extract. The permeate flux, total polyphenols, polyphenolic profile, phenolic acid content, and total antioxidant capacity were determined using the FRAP and DPPH tests, the content of water-soluble proteins during ultrafiltration was established, and the concentration factors and rejections were determined. The permeate flux decreased by increasing the volume reduction ratio and decreasing the molecular weight cut-off of the membranes. The concentration factor and rejection of polyphenolics increased with the increase in the volume reduction ratio (VRR) for all membranes and both liquids. The concentration and rejection effectiveness of the 1 kDa membrane was higher than those observed for 10 and 25 kDa during the ultrafiltration of the apple extract, while these values were comparable for 1 and 10 kDa during the ultrafiltration of the apple juice. The concentration factors and rejections of total polyphenols were higher in the extract than in the juice. Chlorogenic acid was the main compound in the polyphenol profile of apple juice. The total content of phenolic acids, determined by using HPLC, increased by 15–20% as a result of the membrane concentration, but the separation process did not significantly change the ratio between the individual compounds.
Biologically active peptides (BAP) are increasingly in the focus of scientific research due to their widespread use in medicine, food and pharmaceutical industries. Researching and studying the properties of peptides is a laborious and expensive process. In recent years, in silico methods, including data mining or artificial intelligence, have been applied more and more to reveal biological, physicochemical and sensory properties of peptides. This significantly shortens the process of peptide sequences analysis. This article presents a software tool that uses a data mining approach to discover a number of physicochemical properties of a specific peptide. Working with it is extremely simple - it is only necessary to input the amino acid sequence of the peptide of interest. The software tool is designed to generate data in order to increase the classification and prediction accuracy, as well as to leverage the engineering of new amino acid sequences. This way, the proposed software greatly facilitates the work or scientific researchers. The software application is publicly available at www.pep-lab.info/dmpep.
This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.
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