The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Application of Near-Infrared Spectroscopy as an Alternative to Chemical and Color Analysis To Discriminate the Production Chains of Asiago d'Allevo Cheese
A near-infrared spectroscopy (NIRS) application was developed to discriminate Asiago d'Allevo, cheese coming from different production chains (alpine farms, mountain and lowland factories). One hundred wheels were collected in different seasons from all productive sites of Asiago d'Allevo: 14 alpine farms and 8 mountain and 13 lowland factories. Samples were analyzed for chemical composition and color and scanned by NIRS (1100-2500 nm). A factorial discriminant analysis based on chemical and color data showed a clear separation between alpine and factory products due to their different fatty acids profile and color. However, cheeses from lowland and mountain factories were undistinguishable. A discriminant analysis using NIRS spectra alone or combined with chemical and color data showed similar results. A final calibration based on NIRS spectra was developed and validated by a set of 7 external samples to discriminate alpine from factory products. This real-time analysis is a reliable alternative to expensive and time-consuming lab determinations.
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