A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms
Joint Authors
Vicente, H.
Salvador, Cátia
Martins, M. Rosário
Caldeira, A. Teresa
Source
International Journal of Analytical Chemistry
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-31
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Amanita ponderosa are wild edible mushrooms that grow in some microclimates of Iberian Peninsula.
Gastronomically this species is very relevant, due to not only the traditional consumption by the rural populations but also its commercial value in gourmet markets.
Mineral characterisation of edible mushrooms is extremely important for certification and commercialization processes.
In this study, we evaluate the inorganic composition of Amanita ponderosa fruiting bodies (Ca, K, Mg, Na, P, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their respective soil substrates from 24 different sampling sites of the southwest Iberian Peninsula (e.g., Alentejo, Andalusia, and Extremadura).
Mineral composition revealed high content in macroelements, namely, potassium, phosphorus, and magnesium.
Mushrooms showed presence of important trace elements and low contents of heavy metals within the limits of RDI.
Bioconcentration was observed for some macro- and microelements, such as K, Cu, Zn, Mg, P, Ag, and Cd.
A.
ponderosa fruiting bodies showed different inorganic profiles according to their location and results pointed out that it is possible to generate an explanatory model of segmentation, performed with data based on the inorganic composition of mushrooms and soil mineral content, showing the possibility of relating these two types of data.
American Psychological Association (APA)
Salvador, Cátia& Martins, M. Rosário& Vicente, H.& Caldeira, A. Teresa. 2018. A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms. International Journal of Analytical Chemistry،Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1166422
Modern Language Association (MLA)
Salvador, Cátia…[et al.]. A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms. International Journal of Analytical Chemistry No. 2018 (2018), pp.1-18.
https://search.emarefa.net/detail/BIM-1166422
American Medical Association (AMA)
Salvador, Cátia& Martins, M. Rosário& Vicente, H.& Caldeira, A. Teresa. A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms. International Journal of Analytical Chemistry. 2018. Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1166422
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1166422