Two strains (POM1 and C2) or LP09 of Lactobacillus plantarum, which were previously isolated from tomatoes and carrots, and another commercial strain of L. plantarum (LP09), were selected to singly ...ferment (30°C for 120h) pomegranate juice (PJ) under standardized protocol. PJs were further stored at 4°C for 30days. Filtered PJ, not added of starters (unstarted PJ), was used as the control. After fermentation, all starters grew to ca. 9.0LogCFU/mL. Viable cells of strain LP09 sharply decreased during storage. The other two strains survived to ca. 7.0 and 8.0LogCFU/mL. Lactic acid bacteria consumed glucose, fructose, malic acid, and branched chain and aromatic amino acids. The concentration of free fatty acids increased for all started PJs. Compared to unstarted PJ, color and browning indexes of fermented PJs were preferable. The concentration of total polyphenolic compounds and antioxidant activity were the highest for started PJs, with some differences that depended on the starter used. Fermentation increased the concentration of ellagic acid, and enhanced the antimicrobial activity. Fermented PJs scavenged the reactive oxygen species generated by H2O2 and modulated the synthesis of immune-mediators from peripheral blood mononuclear cells (PBMC). Unstarted and fermented PJs inhibited the growth of K562 tumor cells. The sensory attributes of fermented PJs were preferred. The fermentation of pomegranate juice would represent a novel technology option, which joins health-promoting, sensory and preservative features to exploit the potential of pomegranate fruits.
•Lactobacillus plantarum POM1 and C2 were suitable starters for pomegranate juice fermentation.•Pomegranate juice highlighted some peculiar metabolic traits of lactic acid bacteria.•Lactic acid fermentation increased the concentration of ellagic acid.•The fermented pomegranate juice improved health-promoting, sensory and preservative features.
In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. ...Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.
