Tracking antioxidant properties and color changes in low-sugar bilberry jam as effect of processing, storage and pectin concentration

<p>Abstract</p> <p>Background</p> <p>Recently, an increased interest in the identification of valuable possibilities for preserving the antioxidant properties of products obtained by thermal processing of fruits rich in bioactive compounds can be noticed. In this regard, an extensive analysis is necessary in terms of thermal processed products behavior in relation to various factors. The purpose of the present study was to assess the effect which processing and storage at 20°C has on the antioxidant properties and color quality of low-sugar bilberry jam with different low-methoxyl pectin (LMP) concentrations.</p> <p>Results</p> <p>For all measured parameters, it should be noted that thermal processing induced significant alterations reported to the values registered for fresh fruit. Most important losses due to thermal processing were recorded for total monomeric anthocyanins (TMA) (81-84%), followed by L-ascorbic acid (L-AsAc) content (53-58%), total phenolics (TP) content (42-51%) and FRAP (ferric reducing antioxidant power) values (36-47%). Moreover, depreciation of the investigated compounds occurred during storage at 20°C. Jam storage for 7 months resulted in severe losses in TMA content in the range 58-72% from the value recorded one day after processing. This coincided with marked increases in polymeric color percent of these products after 7 months of storage. Also, bilberry jam storage for 7 months resulted in a decrease in L-AsAc content of 40-53% from the value recorded one day after processing, 41-57% in TP content and 33-46% from the value recorded one day after processing for FRAP values. By decreasing of LMP concentration in the jam recipe from 1 to 0.3% there has been an increase in losses of investigated compounds.</p> <p>Conclusion</p> <p>Overall, the results indicated that bilberry jams can also represent a good source of antioxidant compounds, although compared to the fruit, important losses seem to occur. Practical application of this work is that this kind of information will be very useful in optimizing the jam processing technology and storage conditions, in order to improve the quality of these products.</p

Exploiting sensitivity analysis in Bayesian networks for consumer satisfaction study.

The paper presents an application of Bayesian network technology in a empirical customer satisfaction study. The findings of the study should provide insight as to the importance of product/service dimensions in terms of the strength of their influence on overall satisfaction. To this end we apply a sensitivity analysis of the model’s probabilistic parameters, which enables us to classify the dimensions with respect to their (non) linear and synergy effects on low and high overall satisfaction judgments. Selected results from a real-world case study are shown to demonstrate the usefulness of the approach

Data Fusion and Machine Learning for Innovative GNSS Science Use Cases

The volume of data produced worldwide is growing rapidly, from 33 zettabytes in 2018 to an expected 175 zettabytes in 2025 Furthermore, the way in which data are stored and processed will change dramatically over the coming 5 years. Today 80% of the processing and analysis of data takes place in centralised computing facilities, and 20% in smart connected objects, such as cars, home appliance, manufacturing robots and computing facilities close to the user ('edge computing'). By 2025 these proportions are likely to be inverted. In the GNSS space segment, according to current development plans, over 120 GNSS satellites (including European Galileo satellites) will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis. This unique opportunity for science has been recognised by the European Space Agency (ESA) with the creation of the Navigation Science Office, which leverages on GNSS infrastructure to deliver innovative solutions across Earth Science, Space Science, Metrology and Fundamental Physics domains. At the core of this initiative, the GNSS Science Support Centre (GSSC) combines Big Data and Machine learning (ML) technologies to extract knowledge and discover patterns between GNSS-related inputs and outputs given the sheer volume of data. In this work, we introduce key GNSS Science Use Cases, providing a detailed view of GSSC on-going initiatives regarding troposphere and ionosphere characterisation through ML science pipelines to exploit a unique, publicly available repository of multi-faceted GNSS data and products