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首页> 外文期刊>International journal of analytical chemistry >A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms
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A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms

机译:一种改善氨基塔山粥蘑菇无机特征的数据挖掘方法

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Amanita ponderosa are wild ediblemushrooms 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.
机译:Amanita Ponderosa是野生ediblemushrooms,在伊比利亚半岛的一些微界面中生长。对于农村人口的传统消费而且,这一物种而言,这一物种也非常重要,但它在美食市场中的商业价值。食用蘑菇的表征非常重要认证和商业化进程。在这项研究中,我们评估了氨基塔丘素果实的无机组成(Ca,K,Mg,Na,P,Ag,Al,Ba,Cd,Cr,Cu,Fe,Mn,Pb和Zn)及其各自的土壤来自西南伊伯利亚半岛(例如,Alentejo,Andaalusia和extremadura)的24个不同的抽样场所的基材.mineral组合物在宏观中揭示了高含量,即钾,磷和镁.. ushrooms显示出重要的微量元素和低含量对于一些宏观和微量元素,观察到RDI.bioconcoctration的限制范围内的重金属,例如K,Cu,Zn,Mg,P,Ag和Cd.a。 Ponderosa果实体根据它们的位置显示不同的无机谱,结果指出,可以产生基于蘑菇和土壤矿物质含量的无机组成的数据进行的分割的解释性模型,显示出与这两种类型相关的可能性数据的。

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