A Data Mining Approach to Improve Inorganic...

Research ArticleA Data Mining Approach to Improve Inorganic Characterizationof Amanita ponderosaMushrooms

Caacutetia Salvador1 M Rosaacuterio Martins12 Henrique Vicente 23 and A Teresa Caldeira 12

1HERCULES Laboratory Evora University Largo Marques de Marialva 8 7000-809 Evora Portugal2Departamento de Quimica School of Sciences and Technology Evora University Rua Romao Ramalho 59 7000-671 Evora Portugal3Centro ALGORITMI Universidade do Minho Braga Portugal

Correspondence should be addressed to A Teresa Caldeira atcuevorapt

Received 30 April 2017 Revised 12 November 2017 Accepted 14 December 2017 Published 31 January 2018

Academic Editor Gunther K Bonn

Copyright copy 2018 Catia Salvador et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Amanita ponderosa are wild edible mushrooms that grow in somemicroclimates of Iberian Peninsula Gastronomically this speciesis very relevant due to not only the traditional consumption by the rural populations but also its commercial value in gourmetmarkets Mineral characterisation of edible mushrooms is extremely important for certification and commercialization processesIn this study we evaluate the inorganic composition of Amanita ponderosa fruiting bodies (Ca K Mg Na P Ag Al Ba Cd CrCu Fe Mn Pb and Zn) and their respective soil substrates from 24 different sampling sites of the southwest Iberian Peninsula(eg Alentejo Andalusia and Extremadura) Mineral composition revealed high content in macroelements namely potassiumphosphorus and magnesium Mushrooms showed presence of important trace elements and low contents of heavy metals withinthe limits of RDI Bioconcentration was observed for some macro- and microelements such as K Cu Zn Mg P Ag and Cd Aponderosa fruiting bodies showed different inorganic profiles according to their location and results pointed out that it is possibleto generate an explanatory model of segmentation performed with data based on the inorganic composition of mushrooms andsoil mineral content showing the possibility of relating these two types of data

1 Introduction

Mushrooms are known from ancient times for their medic-inal properties and gastronomic properties Therefore theconsumption of edible wild-growing mushrooms has beenvery popular The demand for the commercialization ofedible wild mushrooms has proved to be a widely expandingbusiness with increasing economic importance in manyrural areas of some countries In recent years the con-sumption of edible mushrooms has been increasing andgaining prominence due to their gastronomic potential alsofor their both organoleptic properties (texture and pleasantaroma) [1ndash4] their chemical composition mineral contentand nutraceutical value [4ndash8] Mushrooms are an importantsource of proteins dietary fibres and vitamins (B C D E)containing low levels of sugar and fats They can assimilatelarge amounts of water and minerals such as phosphorusiron potassium cadmiummagnesium copper and zinc due

to the large area of mycelium overgrowing the surface layerof soil [9] This mycelia network is ideally suited to penetrateand access soil pore spaces and an extensive surface area offungal hyphae and physiology enable for many species oneffective absorption and bioconcentration of various metallicelements metalloids and nonmetals [10] Bioconcentrationfactor (BCF) the ratio of the element content in fruitingbody to the content in underlying substrate can expressthe ability of fungi to accumulate elements from substrateand this capacity of the mushroom is affected by fungallifestyle age of fruiting body specific species and elementand environment such as pH organic matter and pollution[9] Moreover the symbiotic relationships that some mush-rooms species namelyA ponderosa can establish with someplants of their habitats allowing the accumulation of highconcentrations of some metals Therefore mineral contentand organic composition of ediblemushrooms are dependenton the species and the characteristics of the ecosystems in

HindawiInternational Journal of Analytical ChemistryVolume 2018 Article ID 5265291 18 pageshttpsdoiorg10115520185265291

2 International Journal of Analytical Chemistry

38∘35

01

7

∘51

46

Figure 1 Geographic representation of sampling location areas of the A ponderosa fruiting bodies of forest area of the Alentejo Andalusiaand Extremadura regions In detail we observe the sampling site of Evora region (Alentejo Portugal) showing the surrounding vegetationof Quercus suber Cistus ladanifer and a sample of the fruiting body collected at this local

which they are inserted [11 12] Some minerals are essentialelements for the correct human body function althoughothers may present toxicity [5 6 9 11 13 14] There aremany species of edible mushrooms growing wild and someof them are slightly characterized about minerals contentand the potential of heavy metals bioaccumulation such aslead mercury cadmium and silver [5 13 15ndash17] On theother hand due to the great diversity of wild mushrooms thesimilarity between some species and the lack of knowledge ofothers may lead to intoxication when harvesting wild speciesleading in some cases to death [18]

The genus Amanita is one of the best known from Agari-cales order and comprises edible and poisonous mushroomsdistributedworldwide occupyingmainly amycorrhizal habi-tat and playing a significant role in forest ecosystems [19 20]This genus includes important species of edible mushroomsas is the case of Amanita ponderosa [18 20ndash24] The southof Portugal due to its Mediterranean characteristics anddiversity of flora is one of the regions of Europewith a greaterpredominance of A ponderosa wild edible mushrooms [23]These robust basidiomata grow during spring in mountedareas with acid soils in forests of holm oaks and cork treeslike Quercus ilex and Quercus suber and with shrubs likeCistus ladanifer Cistus laurifolius and Lavandula stoechasestablishing a symbiotic relationship with them (Figure 1)Gastronomically this species is very relevant not only dueto the traditional consumption in the rural populations butalso due to its commercial value in the gourmet marketshaving high exportation potential in Portugal thus thechemical and mineral characterization of numerous speciesof edible mushrooms for certification purposes and furthercommercialization process becoming of extreme importance

In recent years some artificial intelligence based toolsnamely Data Mining Artificial Neural Networks (ANNs)and Decision Trees (DTs) have been applied for fungalenvironment systems [18 25 26] Data mining tools wereused in latest studies aboutA ponderosamushrooms in orderto establish a bridge of inorganic contents molecular finger-prints and geographical sites In one study a segmentationmodel based on the molecular analysis was developed whichallowed relating the clusters obtained to the geographicalsite of sampling There were also developed explanatorymodels of the segmentation using Decision Trees to relatethe molecular and inorganic data following two differentstrategies one based on DNA profile and another based onthemineral composition [22] Another study based on ANNsexposed the selectedmodel and can predict molecular profilebased on inorganic composition with a good match betweenthe observed and predicted values [18]

The aim of this study was to determine the inorganiccomposition of fruiting bodies of A ponderosa mushroomsfrom different sampling sites in the southwest of the IberianPeninsula analysing the variations in mineral content Addi-tionally the mineral composition of the corresponding soilsubstrates was analysed in order to correlate with the mineralcomposition of the fruiting bodies In the present work thek-means clustering method was used to study the mineralcomposition of A ponderosa fruiting bodies To explain thesegmentationmodel that is in order to obtain rules to assigna case to a cluster DTs were used

2 Materials and Methods

21 Sampling Collection Fruiting bodies of the Amanitaponderosa mushrooms were collected in spring between

International Journal of Analytical Chemistry 3

Table 1 Sampling sites description of A ponderosamushrooms and soil substrates

Sampling sites GPS coordinates Regioncountry(1) Almendres 38∘3310158405710158401015840N 8∘0210158404010158401015840O

Alentejo Portugal(2) Azaruja 38∘4210158401010158401015840N 7∘4510158405810158401015840O(3) Baleizao 38∘0110158403410158401015840N 7∘4210158403810158401015840O(4) Beja 38∘0210158404410158401015840N 7∘5010158405610158401015840O(5) Cabeca Gorda 37∘5510158401910158401015840N 7∘4810158404710158401015840O(6) Cabezas Rubias 37∘4310158405010158401015840N 7∘0510158401110158401015840O Andaluzia Spain(7) Evora 38∘3510158400110158401015840N 7∘5110158404610158401015840O

Alentejo Portugal

(8) Evoramonte 38∘4610158402210158401015840N 7∘4210158404510158401015840O(9) Herde da Mitra 38∘3110158403510158401015840N 8∘0010158405110158401015840O(10) Mertola 37∘3710158404410158401015840N 7∘3910158403010158401015840O(11) Mina S Domingos 37∘4110158400210158401015840N 7∘2810158404910158401015840O(12) Mte da Borralha 37∘5810158403110158401015840N 7∘3710158402210158401015840O(13) Mte Novo 38∘3010158403910158401015840N 7∘4310158400610158401015840O(14) Montejuntos 38∘3210158402410158401015840N 7∘1910158404910158401015840O(15) N Sra Guadalupe 38∘3310158404710158401015840N 8∘0110158402310158401015840O(16) N Sra Machede 38∘3510158402110158401015840N 7∘4810158401910158401015840O(17) Rosal de la Frontera 37∘5710158402110158401015840N 7∘1310158401210158401015840O Andaluzia Spain(18) Sto Aleixo da Restauracao 38∘0410158400110158401015840N 7∘0910158404610158401015840O

Alentejo Portugal

(19) S Miguel de Machede 38∘3710158403410158401015840N 7∘4210158403310158401015840O(20) Serpa 37∘5510158405910158401015840N 7∘3510158400510158401015840O(21) Ve Rocins 37∘5210158401510158401015840N 7∘4410158404110158401015840O(22) Valverde 38∘3210158402410158401015840N 8∘0110158401810158401015840O(23) V N S Bento 37∘5610158402710158401015840N 7∘2310158405110158401015840O(24) Villanueva del Fresno 38∘2210158404910158401015840N 7∘1110158403510158401015840O Extremadura Spain

February and April from 24 different sampling sites inthe southwest of the Iberian Peninsula namely 11 samplescollected from Alto Alentejo region and 10 from BaixoAlentejo Portugal 2 samples collected from Andalusia and1 from Extremadura Spain (Table 1) These mushrooms werecollected in acid soils in forests of Quercus suber Q ilex sspballota Cistus ladanifer and Cistus laurifolius (Figure 1)

Three individuals in the same growth stage were selectedper each sampling site to avoid the effect of size Fruitingbodies were identified by a specialist based onmorphologicalfeatures according to taxonomic description of A ponderosa[27] During the collection process fruiting bodies wereplaced in wicker baskets and samples were transported inrefrigerated containers and deposited in the BiotechnologyLaboratory of Chemistry Department of University of Evora(Portugal) In parallel soil samples were randomly collectedfrom the surrounding soil of fruiting bodies of each site

Fruiting bodies samples were weighed and carefullywashed with double distilled water and representative sam-ples of each sampling site were catalogued stored in sterilebags and preserved at minus20∘C for its inorganic study Thesheath was eliminated during the pretreatment of fruitingbodies samples since it is not usually consumed

Soil substrates were sampled (0ndash15 cm) after removingsome visible organisms small stones sticks and leavesSamples were air-dried in room temperature for 1-2 weeks

and subsequently sieved through a pore size of 2mm andstored at minus4∘C

22 Inorganic Characterization Mineral composition wasdetermined in A ponderosa fruiting bodies and respectivesoil collected in 24 different sampling sites described usingdifferent analytical techniques Flame Atomic AbsorptionSpectrometry (FAAS) Flame Emission Photometry (FEP)and UV-Vis molecular absorption spectrometry

221 Treatment of Samples Mineral composition analysisof fruiting bodies was performed by the dry mineralizationmethod [28] Three samples of mushrooms from each sam-pling site (25 g fresh weight) were dried in a furnace at 100∘Cto constant weight (48 h) from which the moisture contentwas calculated Samples were homogenized transferred toporcelain crucibles and incinerated in a muffle furnace(Termolab) at 460∘C for 14 h In order to determine theorganic matter and mineral content in mushrooms samplesashes were weighted at constant weight Afterwards asheswere bleached after cooling by adding 2M nitric acid dryingthem on thermostatic hotplates and maintaining them at460∘C for 1 h Ashes recovery was performed with 15mL of01M nitric acid and stored at 4∘C until analyses

Three soil samples (5 g dw) collected from each samplingsite were cold treated with an extracting solution of 25


nitric acid (HNO3) (40ml) and shaking with orbital agitationfor 24 h at room temperature The extracts obtained afterfiltering through Whatman No 42 filter paper were storedinto polyethylene bottles and stored at 4∘C until analyses [11]

222 Analytical Determinations Aluminium (Al) barium(Ba) calcium (Ca) cadmium (Cd) lead (Pb) copper (Cu)chromium (Cr) iron (Fe) magnesium (Mg) manganese(Mn) silver (Ag) and zinc (Zn) contents were quantified byflame atomic absorption spectrometry (Perkin-Elmer 3100model) with atomization in an air-acetylene flame and singleelement hollow cathode lamps and background correctionwith deuterium lamp for manganese For the determinationsof calcium and magnesium strontium chloride (SrCl2) wasadded to make up a final concentration of 01125 of thesample in order to prevent anionic interferenceswhichmightmodify the result [29]

Sodium (Na) and potassium (K) were quantified byflame emission photometry with a Jenway model PFP7 flameemission photometer [30 31] Phosphorus (P) was quantifiedby vanadomolybdophosphoric acid colorimetric methodusing a spectrophotometer (Hitachi U-3010 model) [32]

All sample determinations were performed in triplicatefor each one of the three independent ash extracts of mush-room samples from 24 sampling sites (119899 = 9) and for threeextracts of each soil substrate (119899 = 9) Concentration valueswere calculated through the standard curves for each elementexpressed in mgkg dw The bioconcentration factor (BCF)value which is the quotient of the concentration in fruitingbodies divided by the concentration in the soil substrates wasdetermined for all minerals in each sampling site

23 Data Mining

231 Cluster Analysis In the present study a cluster analysiswas carried out The technique applied in order to build upclusters was the k-means clustering method [33] and thesoftware used was WEKA [34] In WEKA Simple k-meansalgorithm the normalization of the numerical attributes iscarried out automatically when the Euclidean distance iscomputed A more detailed description of the mentionedalgorithm can be found in Witten [35]

232 Decision Trees In the k-means clustering method clus-ters were formed without information about the groups andtheir characteristics Therefore it is necessary to understandhow the clusters were formed To attain such a purposeDecision Trees (DTs) were used In this study the algorithmused to generate DTs was theWEKA J48 [34] correspondingto the 8th revision of the C45 algorithm The detaileddescription of the J48 algorithm can be found in Witten etal [35]

24 Statistical Analysis Data were evaluated statisticallyusing the SPSS 210 software Windows Copyrightcopy(Microsoft Corporation) by descriptive parameters andby one-way ANOVA in order to determine statisticallysignificant differences at the 95 confidence level (119901 lt 005)The homogeneity of the population variances was confirmed

Moisture917 plusmn 14 Organic content

76 plusmn 14 Minerals07 plusmn 02

dry weight83 plusmn 14

Figure 2 Moisture dry weight and organic and minerals contentof A ponderosa fruiting bodies from 24 different sampling sites

by the Levene test and the multiple comparisons of mediawere evaluated by Tukeyrsquos test

3 Results

31 Fruiting Bodies and Soil Substratesrsquo Mineral Composi-tion Mineral analysis of A ponderosa mushrooms samplescollected from 24 sampling sites of Alentejo (Portugal)Andalusia and Extremadura (Spain) regions was evaluated

Table 2 shows the contents in moisture organic contentandminerals present in the samples ofA ponderosa collectedin the different places Moisture content ranged from 895 plusmn00 to 938plusmn05 (Table 2)The analyses of variance (one-wayANOVA) showed that A ponderosa fruiting bodies samplescollected in Almendres (1) and Serpa (20) presented signif-icantly higher values compared to the other samples whilethe sample from Mina S Domingos (11) showed moisturecontent significantly lower than the remaining samples Dryweight values for several samples ranged between 62 plusmn 05and 105plusmn01 consisting in organic content values between58plusmn03 and 98plusmn01andminerals between 05plusmn00 and 14plusmn01 Fruiting bodies samples collected in Almendres (1) andSerpa (20) presented values of organic content significantlylower and the sample collected in the Mina S Domingos (11)significantly higher than all analysed samples (119901 lt 005) Themineral content of the fruiting bodies collected in CabezasRubias (6) was 14 plusmn 01 a value significantly higher thanthe remaining samples However the samples from Serpa(20) andVillanueva del Fresno (24) showed the lowest valuessignificantly different from the another samples (119901 lt 005)

Figure 2 shows the average contents of water dry weightorganic content and minerals present in the 24 samples ofA ponderosa analysed The fruiting bodies presented watercontent corresponding to 903 to 931 of their fresh weightThe organic matter content ranged between 62 and 9 andmineral content between 05 and 09 Thus 100 g of edibleA ponderosamushrooms corresponds to amaximumof 9 g ofmacronutrients such as lipids carbohydrates and proteinsand less than 1 g of mineral contentThese values were similarto those observed in a study with mushrooms harvested insome regions of Andalusia (water content 878 organiccontent 118 carbohydrates 66 proteins 32 lipids 05fibre 15 and mineral content 04) [24] and these fruiting


Table 2 Moisture organic content and minerals contents of A ponderosa fruiting bodies from different sampling sites

Fruiting bodies Composition in fresh weightSampling sites Moisture () Organic content () Minerals ()(1) Almendres 936 plusmn 03a 58 plusmn 03a 06 plusmn 01abc

(2) Azaruja 924 plusmn 05abcd 67 plusmn 05abc 09 plusmn 00bc

(3) Baleizao 907 plusmn 03bcde 86 plusmn 04bcd 07 plusmn 01abc

(4) Beja 910 plusmn 00abcde 84 plusmn 01abcd 06 plusmn 00abc

(5) Cabeca Gorda 916 plusmn 07abcde 78 plusmn 07abcd 05 plusmn 00ab

(6) Cabezas Rubias 918 plusmn 04abcde 68 plusmn 01abc 14 plusmn 01d

(7) Evora 902 plusmn 16cde 92 plusmn 14cd 07 plusmn 02abc

(8) Evoramonte 904 plusmn 04bcde 88 plusmn 07bcd 08 plusmn 03abc

(9) Herde da Mitra 905 plusmn 03bcde 89 plusmn 03cd 06 plusmn 01abc

(10) Mertola 932 plusmn 03ab 60 plusmn 03ab 08 plusmn 01abc

(11) Mina S Domingos 895 plusmn 00e 98 plusmn 01d 08 plusmn 01abc

(12) Mte da Borralha 915 plusmn 15abcde 78 plusmn 15abcd 07 plusmn 01abc

(13) Mte Novo 917 plusmn 12abcde 76 plusmn 11abcd 07 plusmn 01abc

(14) Montejuntos 927 plusmn 04abcd 67 plusmn 04abc 05 plusmn 01ab

(15) N Sra Guadalupe 914 plusmn 11abcde 79 plusmn 13abcd 07 plusmn 02abc

(16) N Sra Machede 914 plusmn 15abcde 80 plusmn 14abcd 07 plusmn 02abc

(17) Rosal de la Frontera 904 plusmn 17bcde 88 plusmn 16bcd 08 plusmn 01abc

(18) Sto Aleixo da Restauracao 911 plusmn 11abcde 81 plusmn 11abcd 08 plusmn 01abc

(19) S Miguel de Machede 927 plusmn 04abcd 65 plusmn 03abc 07 plusmn 01abc

(20) Serpa 938 plusmn 05a 58 plusmn 05a 05 plusmn 00a

(21) Ve Rocins 930 plusmn 07abc 64 plusmn 07abc 06 plusmn 01abc

(22) Valverde 920 plusmn 16abcde 71 plusmn 16abcd 09 plusmn 00c

(23) V N S Bento 900 plusmn 02de 91 plusmn 02cd 08 plusmn 00abc

(24) Villanueva del Fresno 931 plusmn 06ab 64 plusmn 05abc 05 plusmn 01a

Values of each determination represents mean plusmn SD (119899 = 9) Different letters following the values indicate significant differences (119901 lt 005)

bodies have a caloric content of 42 kcal100 g of mushroomsimilar to other edible mushroom species characterized aslow calorie foods [8 28 36]

Tables 3ndash6 show the mineral content of 24 samplingsites of A ponderosa fruiting bodies as well as their cor-responding soil samples The studied A ponderosa fruitingbodies showed higher mineral content in macroelementscalcium (Ca)magnesium (Mg) sodium (Na) potassium (K)phosphorus (P) and microelements aluminium (Al) copper(Cu) and iron (Fe) (Tables 3 and 4) Potassiumwas present inhigher concentrations and was higher in Cabezas Rubias (6)fruiting bodies 69565 plusmn 362mgkg dw The samples showedlower values of the trace elements silver (Ag) barium (Ba)cadmium (Cd) chromium (Cr) manganese (Mn) lead (Pb)and zinc (Zn) (Tables 5 and 6)

Minerals contents of A ponderosa fruiting bodies andtheir corresponding soil samples presented significant dif-ferences for all the studied elements (119901 lt 005) CabezasRubias (6) presented significantly higher aluminium andcalcium contents (119901 lt 005) The cadmium content wassignificantly higher (119901 lt 005) in the fruiting bodies collectedin N Sra Machede (16) and Almendres (1) Samples fromEvora (7) Cabezas Rubias (6) and Villanueva del Fresno (24)showed similar chromium contents (119901 gt 005) which were

significantly higher than the others A ponderosa fruitingbodies (119901 lt 005) Results obtained for soil mineral contentshowed some significant differences between sampling sites(119901 lt 005) The sample collected in N Sra Machede (16)presented significant different contents of silver aluminiumand magnesium (119901 lt 005) The Montejuntos sample(14) presented significantly differences of aluminium ironphosphorus and magnesium contents (119901 lt 005) andVillanueva del Fresno (24) presented significant differencesfor barium andmanganese (119901 lt 005) Significant differencesin copper content were observed for Herde da Mitra sample(9) Mertola sample (10) presented significant differences inzinc and Baleizao sample (3) in potassium contents (119901 lt005) Serpa samples (20) presented significant differencesin calcium chromium iron magnesium sodium and zinc(119901 lt 005) Almendres (1) Evora (7) Mte Novo (13) andValverde (22) samples did not present significant differencesfor the iron content (119901 gt 005) Samples of Cabezas Rubias(6) Serpa (20) and Rosal de la Frontera (17) did not presentsignificant differences in lead content (119901 gt 005) Concerningphosphorus content two groups with no significant differ-ences (119901 gt 005) were observed one including Baleizao (3)and Rosal de la Frontera (17) and the other group includingSto Aleixo Restauracao (18) S Miguel de Machede (19) and


Table 3 Ca Mg Na and K mineral content of A ponderosa fruiting bodies and corresponding soil substrates from different sampling sites

Sampling site Sample Minerals (mgKg dry weight)Ca K Mg Na

(1) Almendres FB 757 plusmn 156a 26677 plusmn 915abcde 774 plusmn 130abc 1408 plusmn 146a

SS 106 plusmn 11ab 363 plusmn 9ab 286 plusmn 14abcd 70 plusmn 1abcd

(2) Azaruja FB 139 plusmn 13b 4589 plusmn 103a 194 plusmn 10a 216 plusmn 135f

SS 102 plusmn 12ab 981 plusmn 37g 324 plusmn 29ab 75 plusmn 4abhi

(3) Baleizao FB 606 plusmn 61a 25758 plusmn 4728abcde 856 plusmn 107abcd 2249 plusmn 231gh

SS 162 plusmn 11cd 1099 plusmn 24h 312 plusmn 38ab 109 plusmn 4k

(4) Beja FB 615 plusmn 1a 6090 plusmn 34ab 734 plusmn 1abc 1540 plusmn 17ag

SS 189 plusmn 14de 619 plusmn 19de 277 plusmn 33abcde 69 plusmn 2abcde

(5) Cabeca Gorda FB 554 plusmn 21a 38652 plusmn 1607cde 848 plusmn 159abc 889 plusmn 38abcdef

SS 108 plusmn 6ab 384 plusmn 16abc 186 plusmn 8ef g 69 plusmn 5abcde

(6) Cabezas Rubias FB 352 plusmn 2c 69565 plusmn 362f 1265 plusmn 1cd 2645 plusmn 18h

SS 118 plusmn 16abc 443 plusmn 9c 277 plusmn 19abcde 62 plusmn 2cdef

(7) Evora FB 605 plusmn 1a 34630 plusmn 186cde 713 plusmn 1abc 1241 plusmn 16ab

SS 91 plusmn 23a 651 plusmn 9e 171 plusmn 25f g 52 plusmn 3f g

(8) Evoramonte FB 733 plusmn 15a 25148 plusmn 4556abcde 760 plusmn 101abc 614 plusmn 131bcdef

SS 102 plusmn 8ab 576 plusmn 16d 156 plusmn 11g 51 plusmn 2g

(9) Herde da Mitra FB 579 plusmn 111a 31852 plusmn 5364abcde 685 plusmn 89abc 930 plusmn 58abcdef

SS 159 plusmn 8cd 229 plusmn 9jk 298 plusmn 29abc 51 plusmn 3g

(10) Mertola FB 370 plusmn 57ab 18021 plusmn 1806abc 658 plusmn 33abc 484 plusmn 171cdef

SS 112 plusmn 8abc 731 plusmn 19f 193 plusmn 18def g 82 plusmn 2hij

(11) Mina S Domingos FB 582 plusmn 1a 33607 plusmn 173bcde 737 plusmn 1abc 1097 plusmn 14abcd

SS 143 plusmn 25bcd 256 plusmn 16ij 365 plusmn 43a 59 plusmn 5ef g

(12) Mte da Borralha FB 448 plusmn 33ab 26099 plusmn 1618abcde 696 plusmn 29abc 368 plusmn 70ef

SS 70 plusmn 14a 155 plusmn 9l 172 plusmn 23f g 67 plusmn 5bcde

(13) Mte Novo FB 436 plusmn 72ab 25763 plusmn 1608abcde 562 plusmn 114ab 379 plusmn 25ef

SS 148 plusmn 19bcd 603 plusmn 56de 217 plusmn 34cdef g 53 plusmn 4f g

(14) Montejuntos FB 412 plusmn 50ab 41285 plusmn 982cde 742 plusmn 53abc 1146 plusmn 304abc

SS 224 plusmn 16ef 256 plusmn 16ij 910 plusmn 25j 85 plusmn 2ij

(15) N Sra Guadalupe FB 408 plusmn 11ab 34814 plusmn 1763cde 930 plusmn 158bcd 551 plusmn 52bcdef

SS 148 plusmn 8bcd 656 plusmn 16e 255 plusmn 14bcdef 73 plusmn 1abh

(16) N Sra de Machede FB 632 plusmn 111a 24717 plusmn 1881abcde 843 plusmn 120abc 2185 plusmn 438gh

SS 143 plusmn 3bcd 912 plusmn 48g 804 plusmn 48i 80 plusmn 2ahij

(17) Rosal de la Frontera FB 584 plusmn 25a 51996 plusmn 2660ef 1540 plusmn 77d 964 plusmn 453abcde

SS 83 plusmn 9a 160 plusmn 16kl 365 plusmn 11a 80 plusmn 1ahij

(18) Sto Aleixo da Restauracao FB 366 plusmn 1a 18586 plusmn 106def 481 plusmn 1abc 1098 plusmn 103abcd

SS 79 plusmn 8a 773 plusmn 24f 477 plusmn 14h 70 plusmn 2abc

(19) S Miguel de Machede FB 649 plusmn 74a 45011 plusmn 1152cdef 549 plusmn 140ab 897 plusmn 11abcdef

SS 338 plusmn 35abc 315 plusmn 9kl 1466 plusmn 68abcde 158 plusmn 5cdef g

(20) Serpa FB 698 plusmn 148ab 47715 plusmn 5220abc 736 plusmn 86ab 1078 plusmn 174abcde

SS 119 plusmn 16g 171 plusmn 9ai 272 plusmn 15k 60 plusmn 5l

(21) Ve Rocins FB 448 plusmn 269ab 21788 plusmn 1237abcd 418 plusmn 203ab 419 plusmn 212def

SS 113 plusmn 8abc 320 plusmn 16ai 284 plusmn 14abcd 86 plusmn 2j

(22) Valverde FB 521 plusmn 146ab 27256 plusmn 2180abcde 586 plusmn 35abc 895 plusmn 65abcdef

SS 93 plusmn 23a 405 plusmn 24bc 265 plusmn 41bcdef 111 plusmn 3k

(23) V N S Bento FB 599 plusmn 1a 6228 plusmn 30ab 657 plusmn 1abc 1467 plusmn 15a

SS 233 plusmn 11ef 192 plusmn 16jkl 536 plusmn 37h 60 plusmn 2def g

(24) Villanueva del Fresno FB 459 plusmn 13ab 25713 plusmn 5847abcde 738 plusmn 49abc 1451 plusmn 147a

SS 240 plusmn 26f 368 plusmn 16ab 267 plusmn 27bcde 78 plusmn 4ahij

Total FB 523 plusmn 143 29648 plusmn 14908 738 plusmn 261 1092 plusmn 620

SS 143 plusmn 63 484 plusmn 273 381 plusmn 296 75 plusmn 24

FB fruiting bodies SS soil substrate Mean values (119899 = 9) plusmn SD Different letters for each element indicate significant differences with the confidence level of119901 lt 005 (ANOVA Tukeyrsquos test)


Table 4 Al Cu Fe and P mineral content of A ponderosa fruiting bodies and corresponding soil substrates from different sampling sites

Sampling site Sample Minerals (mgKg dry weight)Al Cu Fe P

(1) Almendres FB 349 plusmn 124bcdef 80 plusmn 1ab 66 plusmn 14abc 319 plusmn 37abcde

SS 277 plusmn 8ab 1 plusmn 0a 1547 plusmn 92a 72 plusmn 2ab

(2) Azaruja FB 58 plusmn 8a 30 plusmn 2a 18 plusmn 4a 44 plusmn 18hi

SS 243 plusmn 30a 2 plusmn 0c 2240 plusmn 160b 74 plusmn 4ab

(3) Baleizao FB 357 plusmn 29bcdef 356 plusmn 37abc 27 plusmn 8a 525 plusmn 62jk

SS 397 plusmn 4cde 7 plusmn 0k 3093 plusmn 92def 156 plusmn 4g

(4) Beja FB 449 plusmn 13def g 373 plusmn 1bc 227 plusmn 1f g 212 plusmn 1def g

SS 254 plusmn 15a 9 plusmn 1l 4107 plusmn 92ij 133 plusmn 8f

(5) Cabeca Gorda FB 151 plusmn 25ab 161 plusmn 10ab 51 plusmn 12abc 447 plusmn 2aj

SS 196 plusmn 4k 1 plusmn 0a 5067 plusmn 244k 130 plusmn 3ef

(6) Cabezas Rubias FB 932 plusmn 77h 140 plusmn 1ab 193 plusmn 1ef 73 plusmn 1ghi

SS 375 plusmn 8cd 5 plusmn 0g 3520 plusmn 160ef gh 93 plusmn 4c

(7) Evora FB 208 plusmn 11abcd 198 plusmn 1ab 292 plusmn 1gh 201 plusmn 1def gh

SS 368 plusmn 18c 2 plusmn 0bc 1653 plusmn 92a 82 plusmn 5bc

(8) Evoramonte FB 423 plusmn 49cdef g 124 plusmn 20ab 62 plusmn 7abc 469 plusmn 83aj

SS 311 plusmn 6b 2 plusmn 0cd 2400 plusmn 160bc 94 plusmn 1c

(9) Herde da Mitra FB 373 plusmn 26bcdef 193 plusmn 19ab 35 plusmn 10ab 277 plusmn 90bcdef

SS 157 plusmn 6l 10 plusmn 0m 4747 plusmn 92k 115 plusmn 3de

(10) Mertola FB 333 plusmn 36bcde 43 plusmn 15ab 22 plusmn 3a 61 plusmn 21ghi

SS 160 plusmn 8l 5 plusmn 0f g 4853 plusmn 244k 119 plusmn 2def

(11) Mina S Domingos FB 299 plusmn 14abcde 334 plusmn 1abc 62 plusmn 1abc 149 plusmn 1f ghi

SS 417 plusmn 6ef 3 plusmn 0de 3573 plusmn 92f gh 74 plusmn 2ab

(12) Mte da Borralha FB 209 plusmn 42abcd 232 plusmn 63ab 41 plusmn 38ab 417 plusmn 65abcj

SS 159 plusmn 6l 2 plusmn 0c 3840 plusmn 160hi 75 plusmn 5ab

(13) Mte Novo FB 423 plusmn 59cdef g 121 plusmn 16ab 293 plusmn 1a 316 plusmn 76abcdef

SS 300 plusmn 15b 3 plusmn 0cd 1653 plusmn 244a 84 plusmn 4bc

(14) Montejuntos FB 545 plusmn 99ef g 170 plusmn 21ab 62 plusmn 10abc 442 plusmn 2abj

SS 813 plusmn 11j 7 plusmn 0jk 8099 plusmn 251l 294 plusmn 11i

(15) N Sra Guadalupe FB 209 plusmn 73abcd 264 plusmn 23abc 110 plusmn 27bcd 344 plusmn 74abcd

SS 200 plusmn 6k 8 plusmn 0l 4960 plusmn 160k 131 plusmn 5ef

(16) N Sra de Machede FB 313 plusmn 47abcde 148 plusmn 26ab 48 plusmn 13abc 542 plusmn 48jk

SS 632 plusmn 19i 4 plusmn 0ef 3738 plusmn 258ghi 78 plusmn 1abc

(17) Rosal de la Frontera FB 659 plusmn 36g 584 plusmn 51c 119 plusmn 97cde 641 plusmn 98k

SS 381 plusmn 4cd 9 plusmn 0l 3360 plusmn 160ef gh 154 plusmn 4g

(18) Sto Aleixo da Restauracao FB 65 plusmn 12def g 129 plusmn 1ab 51 plusmn 1abc 34 plusmn 1aj

SS 449 plusmn 16f g 6 plusmn 0ghi 2827 plusmn 92cd 185 plusmn 1h

(19) S Miguel de Machede FB 610 plusmn 183f g 207 plusmn 39ab 311 plusmn 10h 270 plusmn 60cdef

SS 497 plusmn 6cd 5 plusmn 0gh 9040 plusmn 139def g 111 plusmn 2h

(20) Serpa FB 453 plusmn 66a 143 plusmn 14ab 43 plusmn 18abc 460 plusmn 16i

SS 378 plusmn 4h 5 plusmn 0f g 3253 plusmn 92m 179 plusmn 7d

(21) Ve Rocins FB 181 plusmn 24abc 84 plusmn 7ab 36 plusmn 24ab 165 plusmn 85ef ghi

SS 391 plusmn 9cde 9 plusmn 0l 3040 plusmn 160de 75 plusmn 5ab

(22) Valverde FB 183 plusmn 54abc 62 plusmn 3ab 57 plusmn 21abc 152 plusmn 34f ghi

SS 453 plusmn 6g 1 plusmn 0ab 1440 plusmn 160a 65 plusmn 2a

(23) V N S Bento FB 303 plusmn 7abcde 172 plusmn 1ab 182 plusmn 1def 197 plusmn 1def ghi

SS 409 plusmn 6de 6 plusmn 0hij 4587 plusmn 244jk 116 plusmn 5de

(24) Villanueva del Fresno FB 602 plusmn 66f g 100 plusmn 9ab 53 plusmn 17abc 310 plusmn 83abcdef

SS 494 plusmn 4h 6 plusmn 0ij 2347 plusmn 92bc 179 plusmn 12h





Table 5 Ag Ba Cd and Cr mineral content of A ponderosa fruiting bodies and corresponding soil substrates from different sampling sites

Sampling site Sample Minerals (mgkg dry weight)Ag Ba Cd Cr

(1) Almendres FB 26 plusmn 05abcde 29 plusmn 11a 22 plusmn 08a 13 plusmn 06abc

SS 01 plusmn 00a 20 plusmn 03ab 03 plusmn 00ab 10 plusmn 02a

(2) Azaruja FB 66 plusmn 22f 09 plusmn 02def g 03 plusmn 00b 06 plusmn 01cde

SS 01 plusmn 00abcd 16 plusmn 02a 03 plusmn 01a 09 plusmn 01a

(3) Baleizao FB 51 plusmn 18abcd 06 plusmn 03def g 11 plusmn 03bcde 10 plusmn 02de

SS 01 plusmn 00abcd 53 plusmn 09i 02 plusmn 00e 24 plusmn 01bcd

(4) Beja FB 11 plusmn 02ab 08 plusmn 00bcdef g 04 plusmn 00bcde 08 plusmn 00a

SS 01 plusmn 00abcdef 26 plusmn 07abcd 01 plusmn 00f 44 plusmn 02ij

(5) Cabeca Gorda FB 21 plusmn 07abc 03 plusmn 00def g 07 plusmn 01cde 08 plusmn 00bcde

SS 02 plusmn 00bcdef 23 plusmn 01abcd 02 plusmn 00cde 42 plusmn 00ghij

(6) Cabezas Rubias FB 15 plusmn 02abc 20 plusmn 03def g 08 plusmn 01cde 09 plusmn 01f

SS 02 plusmn 00cdef g 26 plusmn 04abcd 01 plusmn 00ef 31 plusmn 05cdef gh

(7) Evora FB 05 plusmn 03abcd 08 plusmn 04f g 04 plusmn 03bcde 09 plusmn 05f

SS 02 plusmn 00ghi 43 plusmn 11f ghi 01 plusmn 00ef 28 plusmn 05cdef

(8) Evoramonte FB 10 plusmn 01def 09 plusmn 01bcde 12 plusmn 02bcde 29 plusmn 01bcde

SS 01 plusmn 00abcdef 28 plusmn 01abcde 01 plusmn 00ef 26 plusmn 00cdef

(9) Herde da Mitra FB 07 plusmn 01abcd 12 plusmn 01g 10 plusmn 00bcde 18 plusmn 01bcde

SS 02 plusmn 00ef gh 23 plusmn 01abc 01 plusmn 00ef 38 plusmn 01f ghij

(10) Mertola FB 43 plusmn 08ab 15 plusmn 02bcdef 10 plusmn 03bcd 09 plusmn 03de

SS 01 plusmn 00ab 30 plusmn 04bcdef 01 plusmn 00ef 30 plusmn 02cdef g

(11) Mina S Domingos FB 06 plusmn 01abc 06 plusmn 00cdef g 14 plusmn 02de 07 plusmn 01abcde

SS 03 plusmn 00i 19 plusmn 00ab 01 plusmn 00ef 51 plusmn 07jk

(12) Mte da Borralha FB 15 plusmn 01abc 09 plusmn 01f g 11 plusmn 02bc 09 plusmn 01cde

SS 02 plusmn 00def g 23 plusmn 00abcd 01 plusmn 00ef 37 plusmn 00def ghi

(13) Mte Novo FB 06 plusmn 01a 13 plusmn 02ef g 06 plusmn 01bcde 05 plusmn 01e

SS 01 plusmn 00abc 34 plusmn 02cdef g 02 plusmn 00bcde 26 plusmn 02cdef

(14) Montejuntos FB 15 plusmn 02abc 11 plusmn 01bcd 13 plusmn 01bcde 10 plusmn 01abcd

SS 02 plusmn 01hi 36 plusmn 03def gh 01 plusmn 00ef 57 plusmn 02k

(15) N Sra Guadalupe FB 25 plusmn 03ef 07 plusmn 02def g 09 plusmn 02bcde 05 plusmn 01abcde

SS 02 plusmn 00bcdef g 24 plusmn 01abcd 02 plusmn 00cde 43 plusmn 01hij

(16) N Sra Machede FB 04 plusmn 02abcde 06 plusmn 01bcdef g 07 plusmn 01f 04 plusmn 01bcde

SS 04 plusmn 00j 40 plusmn 02ef ghi 01 plusmn 00ef 38 plusmn 03ef ghij

(17) Rosal de la Frontera FB 16 plusmn 03cde 15 plusmn 01bcdef 10 plusmn 01bc 12 plusmn 00a

SS 02 plusmn 00cdef g 23 plusmn 01abcd 01 plusmn 00ef 29 plusmn 02cdef

(18) Sto Aleixo da Restauracao FB 13 plusmn 03abc 11 plusmn 01def g 07 plusmn 01bc 09 plusmn 00bcde

SS 02 plusmn 00ef gh 28 plusmn 02abcde 03 plusmn 00abcd 28 plusmn 09cdef

(19) S Miguel de Machede FB 26 plusmn 03bcde 13 plusmn 03bcdef g 39 plusmn 05bcde 09 plusmn 01ab

SS 02 plusmn 00ef g 34 plusmn 01cdef g 01 plusmn 00ef 23 plusmn 02bc

(20) Serpa FB 17 plusmn 03abc 04 plusmn 01abc 04 plusmn 01bcde 06 plusmn 01bcde

SS 02 plusmn 00f gh 24 plusmn 03abcd 00 plusmn 00f 79 plusmn 04l

(21) Ve Rocins FB 33 plusmn 22a 13 plusmn 05def g 05 plusmn 02bc 18 plusmn 06bcde

SS 01 plusmn 00abcde 48 plusmn 02hi 02 plusmn 00de 25 plusmn 01bcde

(22) Valverde FB 18 plusmn 06ab 05 plusmn 00def g 10 plusmn 01e 29 plusmn 01bcde

SS 02 plusmn 00cdef g 17 plusmn 03ab 02 plusmn 00bcde 12 plusmn 01ab

(23) V N S Bento FB 04 plusmn 03abc 21 plusmn 02cdef g 07 plusmn 01bcde 28 plusmn 04bcde

SS 01 plusmn 00abcdef 45 plusmn 04ghi 03 plusmn 01abc 18 plusmn 13abc

(24) Villanueva del Fresno FB 30 plusmn 03a 11 plusmn 02ab 08 plusmn 03bcde 15 plusmn 04f

SS 01 plusmn 00abcde 113 plusmn 10j 03 plusmn 00abc 49 plusmn 06ijk





Table 6 Mn Pb and Zn mineral content of A ponderosa fruiting bodies and corresponding soil substrates from different sampling sites

Sampling site Sample Minerals (mgkg dry weight)Mn Pb Zn

(1) Almendres FB 28 plusmn 6abcd 07 plusmn 05ab 59 plusmn 1abcd

SS 22 plusmn 1a 40 plusmn 04a 4 plusmn 0ab

(2) Azaruja FB 6 plusmn 1a 04 plusmn 01a 16 plusmn 2a

SS 131 plusmn 9ab 70 plusmn 10abcde 5 plusmn 1abc

(3) Baleizao FB 91 plusmn 14gh 17 plusmn 05cdef 97 plusmn 2cde

SS 893 plusmn 17f g 92 plusmn 11def 9 plusmn 0d

(4) Beja FB 84 plusmn 1f gh 28 plusmn 01abcd 104 plusmn 1de

SS 507 plusmn 45c 69 plusmn 06abcde 13 plusmn 0g

(5) Cabeca Gorda FB 66 plusmn 6cdef gh 19 plusmn 02cdef 81 plusmn 1bcde

SS 695 plusmn 62de 45 plusmn 02ab 10 plusmn 0d

(6) Cabezas Rubias FB 81 plusmn 1ef gh 51 plusmn 08hi 132 plusmn 1e

SS 735 plusmn 30def 165 plusmn 06g 11 plusmn 1de

(7) Evora FB 59 plusmn 1bcdef gh 19 plusmn 09i 75 plusmn 1bcde

SS 175 plusmn 3ab 109 plusmn 02f 5 plusmn 1bc

(8) Evoramonte FB 79 plusmn 11ef gh 42 plusmn 02ghi 59 plusmn 7abcd

SS 212 plusmn 12b 74 plusmn 03abcdef 6 plusmn 0c

(9) Herde da Mitra FB 47 plusmn 9abcdef g 14 plusmn 02bcdef 61 plusmn 2abcd

SS 725 plusmn 17de 54 plusmn 03abc 11 plusmn 0ef

(10) Mertola FB 29 plusmn 6abcd 37 plusmn 09abcde 52 plusmn 8abcd

SS 725 plusmn 17de 80 plusmn 07bcdef 15 plusmn 1h

(11) Mina S Domingos FB 104 plusmn 1h 31 plusmn 07ef gh 91 plusmn 1cde

SS 290 plusmn 6b 103 plusmn 04 ef 4 plusmn 1a

(12) Mte da Borralha FB 39 plusmn 16abcdef 22 plusmn 03abc 62 plusmn 4abcd

SS 199 plusmn 9b 54 plusmn 03abc 10 plusmn 1de

(13) Mte Novo FB 34 plusmn 4abcd 16 plusmn 03a 52 plusmn 4abcd

SS 139 plusmn 3ab 82 plusmn 07cdef 5 plusmn 0abc

(14) Montejuntos FB 49 plusmn 9abcdef g 30 plusmn 01ef gh 60 plusmn 3abcd

SS 649 plusmn 27cde 102 plusmn 15ef 10 plusmn 0de

(15) N Sra Guadalupe FB 38 plusmn 5abcde 21 plusmn 03abcdef 63 plusmn 10abcd

SS 794 plusmn 232ef 45 plusmn 02ab 12 plusmn 0f g

(16) N Sra Machede FB 50 plusmn 13abcdef g 04 plusmn 01abc 70 plusmn 2abcd

SS 123 plusmn 4ab 84 plusmn 01cdef 9 plusmn 0d

(17) Rosal de la Frontera FB 90 plusmn 56gh 30 plusmn 01cdef g 95 plusmn 6cde

SS 725 plusmn 17de 165 plusmn 06g 6 plusmn 0bc

(18) Sto Aleixo da Restauracao FB 47 plusmn 15cdef gh 39 plusmn 03i 47 plusmn 1abcd

SS 193 plusmn 7b 64 plusmn 09abcd 11 plusmn 0de

(19) S Miguel de Machede FB 91 plusmn 1gh 14 plusmn 01f gh 58 plusmn 8abcd

SS 576 plusmn 17g 58 plusmn 02abcd 9 plusmn 0d

(20) Serpa FB 69 plusmn 19abcdef g 10 plusmn 02def gh 61 plusmn 9abcd

SS 1002 plusmn 30cd 195 plusmn 37g 30 plusmn 1j

(21) Ve Rocins FB 15 plusmn 10ab 23 plusmn 08bcdef 31 plusmn 2ab

SS 893 plusmn 17f g 92 plusmn 11def 5 plusmn 1abc

(22) Valverde FB 26 plusmn 7abc 51 plusmn 02f gh 42 plusmn 2abc

SS 21 plusmn 1a 44 plusmn 04ab 5 plusmn 0abc

(23) V N S Bento FB 72 plusmn 1def gh 16 plusmn 01hi 86 plusmn 1bcde

SS 745 plusmn 45ef 84 plusmn 30cdef 27 plusmn 1i

(24) Villanueva del Fresno FB 39 plusmn 6abcdef 31 plusmn 06abcde 68 plusmn 4abcd

SS 1518 plusmn 45h 74 plusmn 05abcdef 5 plusmn 1abc

Total FB 56 plusmn 27 24 plusmn 13 68 plusmn 25

SS 529 plusmn 378 85 plusmn 40 10 plusmn 6



Ag Al Ba Ca Cd Cr Cu Fe K Mg Mn Na P Pb ZnMinerals

Fruiting bodiesSoils

001

01

1

10

100

1000

10000

100000

Min

eral

cont

ent (

mg

kg d

w)

Figure 3 Concentration of mineral content of A ponderosa fruiting bodies and soil substrates The values are represented in logarithmicscale

Vila Nueva del Fresno (24) samples Finally samples fromBaleizao (3) and Valverde (22) did not present significantdifferences in sodium and potassium content (119901 gt 005)

In fact mineral content of A ponderosa fruiting bodiesand their soil samples (Figure 3) varied very similarly foralmost all the analysed elements indicating the influence ofthe substrate characteristics on the composition of mush-rooms samples collected at the different sites

Mushrooms have a specializedmechanism to accumulatenutrients and minerals in their fruiting bodies The ageof the fruiting body or its size may contribute to mineralcompositionThusA ponderosamushrooms analysed in thisstudy were obtained at the same development stage in orderto eliminate possible size interferences in the comparisonof their mineral content Macroelements content such asCa Mg Na K and P in fruiting bodies were similar tothose reported in literature for A ponderosa [28] and forother edible mushrooms species [5 36ndash38] The calcium andphosphorus contents for the different samples studied were523 plusmn 143 and 294 plusmn 171mgkg dw Potassium and sodiumminerals responsible for the hydroelectrolytic balance main-tenance and important enzyme cofactors have high RDIs(Recommended Daily Intakes) which are 4700mgday and2000mgday respectively for an adult [39] Potassium andsodium levels of A ponderosa mushrooms studied were29648 plusmn 14908 and 1092 plusmn 620mgkg dw respectivelyPotassium content ranged from 18021 plusmn 1806 to 69565 plusmn362mgKg dw presenting values similar to those describedby Moreno-Rojas et al [28] that report potassium levelsranging between 22410 plusmn 211 and 60890 plusmn 23950mgKg dwHowever the low K levels observed for fruiting bodiesfrom three sampling sites can be correlated with the largedifferences observed for potassium content in the surround-ing substrates Magnesium also plays an important role inlarge number of biological functions particularly linked toenergy metabolism it is required for the proper function

of certain enzymes as cofactor and structural functions therecommended magnesium intakes for adult (19ndash65 years)are 220ndash260mgday [40] The mean magnesium content inthe fruiting bodies was 738 plusmn 261mgkg dw similar to thatdescribed in the literature [28]

A ponderosa fruiting bodies presented values of traceminerals smaller than those found in other species of edi-ble mushrooms [6 11 14 38 41] Concentrations of traceelements in fruiting bodies are generally species-dependent[36] however only one study of A ponderosa mushrooms isreported [28] The existence of higher metal concentrationsin younger fruiting bodies can be explained by the transportof metals from the mycelium to the fruiting body duringthe beginning of fructification [36] Trace elements likeFe Cu Zn Cr and Mn are essential metals since theyplay an important role in biological systems however thesetrace metals can also produce toxic effects when intake isexcessively amount [38 42 43] Copper is the third mostabundant trace element found in the human body and beingan important element of several enzymes it is also oneof the agents involved in iron metabolism [42] Iron is acomponent of haemoglobin andmyoglobin proteins respon-sible for transporting oxygen to tissues It also participatesin protein metabolism energy production in cells and invarious enzymatic reactions [44] The copper content was185 plusmn 125mgkg dw and the highest value was found inA ponderosa samples collected from Rosal de la Frontera(17) (584 plusmn 51mgkg dw) The iron content in A ponderosamushroomswas 92plusmn85mgkg dw Copper levels were similarand iron levels are slightly lower than those described forAmanita spp [5 28] The adult RDI is 2mgday for Cu and18mgday for Fe [12 39] so the concentrations of copper andiron present in these edible mushrooms are not consideredto be a health risk Manganese and zinc are importanttrace elements for the human organism participating inmacronutrients and nucleic acids metabolism and promoting


Table 7 Bioconcentration factor (BCF) values for A ponderosa fruiting bodies from different sampling sites

Sampling site Al Ca Cu Fe K Mg Na P(1) Almendres 13 plusmn 04 71 plusmn 07 123 plusmn 2 004 plusmn 001 74 plusmn 1 27 plusmn 03 20 plusmn 2 44 plusmn 04

(2) Azaruja 02 plusmn 00 14 plusmn 00 16 plusmn 1 001 plusmn 000 5 plusmn 1 06 plusmn 00 3 plusmn 2 06 plusmn 02

(3) Baleizao 09 plusmn 01 37 plusmn 01 48 plusmn 3 001 plusmn 000 23 plusmn 4 27 plusmn 00 21 plusmn 1 34 plusmn 03

(4) Beja 18 plusmn 01 33 plusmn 02 43 plusmn 2 006 plusmn 000 10 plusmn 0 27 plusmn 03 22 plusmn 0 16 plusmn 01

(5) Cabeca Gorda 08 plusmn 01 51 plusmn 01 248 plusmn 16 001 plusmn 000 101 plusmn 0 45 plusmn 07 13 plusmn 0 34 plusmn 01

(6) Cabezas Rubias 25 plusmn 02 30 plusmn 04 28 plusmn 1 005 plusmn 000 157 plusmn 2 46 plusmn 03 43 plusmn 1 08 plusmn 00

(7) Evora 06 plusmn 00 70 plusmn 18 122 plusmn 19 018 plusmn 001 53 plusmn 0 42 plusmn 06 24 plusmn 1 25 plusmn 01

(8) Evoramonte 14 plusmn 01 72 plusmn 04 52 plusmn 1 003 plusmn 000 44 plusmn 7 49 plusmn 03 12 plusmn 2 50 plusmn 08

(9) Herde da Mitra 24 plusmn 01 36 plusmn 05 20 plusmn 1 001 plusmn 000 139 plusmn 18 23 plusmn 01 18 plusmn 0 24 plusmn 07

(10) Mertola 21 plusmn 01 33 plusmn 03 9 plusmn 3 000 plusmn 000 25 plusmn 2 34 plusmn 02 6 plusmn 2 05 plusmn 02

(11) Mina S Domingos 07 plusmn 00 42 plusmn 07 114 plusmn 10 002 plusmn 000 132 plusmn 8 20 plusmn 02 19 plusmn 1 20 plusmn 00

(12) Mte da Borralha 13 plusmn 02 65 plusmn 09 119 plusmn 32 001 plusmn 001 168 plusmn 1 41 plusmn 04 5 plusmn 1 55 plusmn 05

(13) Mte Novo 14 plusmn 01 29 plusmn 01 48 plusmn 2 002 plusmn 000 43 plusmn 1 26 plusmn 01 7 plusmn 0 37 plusmn 07

(14) Montejuntos 07 plusmn 01 18 plusmn 01 25 plusmn 1 001 plusmn 000 162 plusmn 6 08 plusmn 00 13 plusmn 3 15 plusmn 01

(15) N Sra Guadalupe 10 plusmn 03 28 plusmn 01 31 plusmn 1 002 plusmn 000 53 plusmn 10 36 plusmn 04 8 plusmn 1 26 plusmn 05

(16) N Sra Machede 05 plusmn 01 44 plusmn 07 39 plusmn 7 001 plusmn 000 27 plusmn 1 11 plusmn 01 27 plusmn 5 69 plusmn 05

(17) Rosal de la Frontera 17 plusmn 01 71 plusmn 05 68 plusmn 4 003 plusmn 003 326 plusmn 16 42 plusmn 01 12 plusmn 6 42 plusmn 05

(18) Sto Aleixo da Restauracao 01 plusmn 00 47 plusmn 05 23 plusmn 1 002 plusmn 000 24 plusmn 1 10 plusmn 00 16 plusmn 1 02 plusmn 00

(19) S Miguel de Machede 12 plusmn 04 19 plusmn 00 38 plusmn 4 003 plusmn 000 142 plusmn 33 04 plusmn 01 6 plusmn 0 24 plusmn 05

(20) Serpa 12 plusmn 02 58 plusmn 05 31 plusmn 1 001 plusmn 001 278 plusmn 16 27 plusmn 02 18 plusmn 1 26 plusmn 00

(21) Ve Rocins 05 plusmn 01 39 plusmn 21 10 plusmn 1 001 plusmn 001 67 plusmn 34 15 plusmn 06 5 plusmn 2 22 plusmn 10

(22) Valverde 04 plusmn 01 56 plusmn 02 83 plusmn 24 004 plusmn 001 65 plusmn 50 22 plusmn 02 8 plusmn 0 23 plusmn 05

(23) V N S Bento 07 plusmn 00 26 plusmn 01 28 plusmn 1 004 plusmn 000 33 plusmn 3 12 plusmn 01 24 plusmn 1 17 plusmn 01

(24) Villanueva del Fresno 12 plusmn 01 19 plusmn 02 16 plusmn 1 002 plusmn 001 70 plusmn 13 28 plusmn 01 19 plusmn 1 17 plusmn 04

Values of each determination represents mean plusmn SD (119899 = 3)

several enzyme activity processes [14 45 46]These elementscan be accumulated by mushrooms and the recommendeddaily intakes were 2mgday and 15mgday for manganeseand zinc respectively [12 39] In this study mushroomspresented a mean value of 56 plusmn 27mgkg dw for manganeseand 68 plusmn 25mgkg dw for zinc content similar to valuesdescribed in literature [5 6 11 36 47] Chromium biologicalfunctions are not known precisely it seems to participatein the metabolism of lipids and carbohydrates as well as inthe insulin action The RDI for this metal is 120 120583gday Themean chromium content obtained was 119 plusmn 074mgkg dwlower than those reported in the literature for other species ofAmanita [5] and similar to some species of ediblemushroomsdescribed [11 36] Aluminium is one of the few abundantelements in nature but no significant biological functionis known although there are some evidences of toxicitywhen ingested in large quantities Most acidic soils aresaturated in aluminium instead of hydrogen ions and thisacidity is the result of aluminium compounds hydrolysis [48]A ponderosa fruiting bodies from the different samplingsites showed a high range of aluminium content with amedium value of 362plusmn204mgKg dw High aluminium levelswere reported for some Amanita species for example Arubescens that showed values around 262mgkg dw [15] andA strobiliformis and A verna presented aluminium levels of72 and 343mgkg dw respectively [5] Other studies reportdifferent levels of aluminium in Amanita rubescens 293 75

and 512mgkg dw for the whole fruiting body cap and stiperespectively [37] The large range in aluminium content wasalso reported in a study of A fulva that showed aluminiumlevels ranging from 40 to 500mgKg dw in the stipe and 40to 200mgKg dw in cap [10] Regarding cadmium and leadthese elements have the highest toxicological significanceCadmiumhas probably been themost damagingmetal foundin mushrooms some studies point out the existence ofaccumulating species which in polluted areas accumulatethis metal The mean values of cadmium and lead were100 plusmn 073mgkg dw and 241 plusmn 134mgkg dw respectivelyshowing that the A ponderosa had no toxicity due to thepresence of these two elements [5 11] The contents of heavymetals barium and silver in the studied mushrooms were110 plusmn 059mgkg dw and 201 plusmn 156mgkg dw respectivelyThese values are similar to those described in the literature forother species of edible mushrooms [11] and lower than thosedescribed for species of the genus Amanita [5 13]

Bioconcentration factor (BCF) allows estimating themushroom potential for the bioextraction of elements fromthe substratum (soil) Values of BCF of A ponderosa fruitingbodies are summarized in Tables 7 and 8ThemacroelementsK andNa exhibited the highest values of BCFbutCaMg andP also presented BCFgt 1 Trace elements Ag Cd Cu and Znpresented BCF gt 1 with higher values for Cu The remainingelements (Fe Mn Ba Cr and Pb) are bioexcluded showinglower values (BCF lt 1) For copper BCF values ranged from



Sampling site Ag Ba Cd Cr Mn Pb Zn(1) Almendres 38 plusmn 4 141 plusmn 033 7 plusmn 2 130 plusmn 039 127 plusmn 022 015 plusmn 010 13 plusmn 0

(2) Azaruja 72 plusmn 8 057 plusmn 004 1 plusmn 0 068 plusmn 003 005 plusmn 000 006 plusmn 000 3 plusmn 0

(3) Baleizao 55 plusmn 8 012 plusmn 004 7 plusmn 0 042 plusmn 007 010 plusmn 001 019 plusmn 003 10 plusmn 0

(4) Beja 8 plusmn 1 033 plusmn 008 15 plusmn 1 019 plusmn 000 017 plusmn 001 041 plusmn 002 8 plusmn 0

(5) Cabeca Gorda 14 plusmn 4 014 plusmn 001 4 plusmn 0 018 plusmn 001 009 plusmn 000 041 plusmn 003 8 plusmn 0

(6) Cabezas Rubias 10 plusmn 1 077 plusmn 001 8 plusmn 1 029 plusmn 000 011 plusmn 000 031 plusmn 004 13 plusmn 1

(7) Evora 2 plusmn 1 019 plusmn 005 4 plusmn 2 030 plusmn 013 034 plusmn 000 017 plusmn 008 15 plusmn 2

(8) Evoramonte 7 plusmn 0 032 plusmn 002 9 plusmn 1 114 plusmn 002 037 plusmn 003 056 plusmn 000 10 plusmn 1

(9) Herde da Mitra 4 plusmn 0 053 plusmn 002 11 plusmn 2 047 plusmn 000 006 plusmn 001 026 plusmn 003 5 plusmn 0

(10) Mertola 61 plusmn 2 050 plusmn 001 8 plusmn 1 032 plusmn 007 004 plusmn 001 046 plusmn 007 3 plusmn 0

(11) Mina S Domingos 2 plusmn 0 030 plusmn 001 13 plusmn 1 013 plusmn 000 036 plusmn 000 030 plusmn 006 25 plusmn 3

(12) Mte da Borralha 9 plusmn 0 040 plusmn 004 12 plusmn 1 024 plusmn 002 019 plusmn 007 041 plusmn 003 6 plusmn 0

(13) Mte Novo 6 plusmn 0 039 plusmn 005 3 plusmn 0 020 plusmn 002 024 plusmn 002 020 plusmn 002 10 plusmn 0

(14) Montejuntos 6 plusmn 1 029 plusmn 000 10 plusmn 2 018 plusmn 001 008 plusmn 001 030 plusmn 004 6 plusmn 0

(15) N Sra Guadalupe 16 plusmn 1 031 plusmn 008 5 plusmn 1 012 plusmn 001 005 plusmn 001 048 plusmn 006 5 plusmn 1

(16) N Sra Machede 1 plusmn 1 014 plusmn 002 8 plusmn 1 010 plusmn 000 040 plusmn 009 005 plusmn 002 7 plusmn 0

(17) Rosal de la Frontera 11 plusmn 2 065 plusmn 001 9 plusmn 1 042 plusmn 002 012 plusmn 007 018 plusmn 000 16 plusmn 1

(18) Sto Aleixo da Restauracao 7 plusmn 0 039 plusmn 000 3 plusmn 0 036 plusmn 010 024 plusmn 007 062 plusmn 004 4 plusmn 0

(19) S Miguel de Machede 16 plusmn 1 038 plusmn 008 38 plusmn 1 041 plusmn 003 016 plusmn 000 023 plusmn 001 6 plusmn 1

(20) Serpa 9 plusmn 0 018 plusmn 002 14 plusmn 3 007 plusmn 001 007 plusmn 002 005 plusmn 000 2 plusmn 0

(21) Ve Rocins 35 plusmn 21 027 plusmn 010 3 plusmn 1 071 plusmn 024 002 plusmn 001 025 plusmn 006 6 plusmn 0

(22) Valverde 12 plusmn 2 031 plusmn 004 5 plusmn 0 238 plusmn 003 123 plusmn 028 116 plusmn 005 9 plusmn 0

(23) V N S Bento 4 plusmn 2 046 plusmn 001 2 plusmn 0 246 plusmn 201 010 plusmn 000 021 plusmn 007 3 plusmn 0

(24) Villanueva del Fresno 32 plusmn 6 010 plusmn 001 3 plusmn 1 030 plusmn 004 003 plusmn 000 042 plusmn 006 14 plusmn 1


9 to 248 showing bioaccumulation of this metal The sameheterogeneous behaviour is observed for Ag Cd and Znwith values ranging from 1ndash72 1ndash38 to 2ndash25 respectivelyAg bioaccumulation was found in other Amanita specieswith much higher values of BCF [13] The high levels ofaluminium observed for some A ponderosa fruiting bodiescan be related to soil content and to their different abilityto accumulate this mineral with BCF gt 1 for some standsSome works with different species such as Leccinum scabrumAmanita rubescens and Xerocomus chrysenteron reporteddifferent aluminium accumulation [15] Some species ofBasidiomycetes can be useful for assessing the environmentalpollution levels [38]

Metal concentrations were usually assumed to be species-dependent but soil composition is also an important factorin mineral content [12 36 38 49] In order to clarify thisassociation between inorganic composition of A ponderosamushrooms and soil a data mining approach was developed

32 Segmentation Models Based on Mushrooms and Soil Min-eral Content Interpretation and Assessment The k-MeansClustering Method is a segmentation algorithm that usesunsupervised learning The input variables used in the seg-mentation approach aremacroelements content (Na K Ca PandMg) trace elements content (Al Fe Cu Zn Cr andMn)and heavy metals (Ag Ba Cd and Pb) The algorithm input

parameter is the number of clusters 3 in this study Table 9shows the clusters centre of gravity in order to characterizethe clusters formed

The analysis of Table 9 shows that cluster 1 is characterizedby high values of Ag Ba Cd K Mg and P Cluster 2 ischaracterized by high content of Cr Fe Mn Na Pb andZn and Cluster 3 showed lower mineral content In orderto evaluate the relationships between clusters and fruitingbodies sampling sites the graph presented Figure 4 wasconceived

The analysis of Figure 4 shows that cluster 1 is formedby the samples collected at Almendres (1) Baleizao (3)Cabeca Gorda (5) Evoramonte (8) Mina S Domingos (11)Montejuntos (14) N Sra Guadalupe (15) N Sra Machede(16) Rosal de la Frontera (17) Serpa (20) and Villanueva delFresno (24) Cluster 2 includes the samples collected at Beja(2) Evora (7) S Miguel de Machede (19) and V N S Bento(23) Finally cluster 3 is composed by the samples collected atAzaruja (2) Herde daMitra (9)Mertola (10)Mte da Borralha(12) Mte Novo (13) Sto Aleixo da Restauracao (18) Ve deRocins (21) and Valverde (22)

In order to generate an explanatory model of segmen-tation (ie seek to establish rules for assigning a new caseto a cluster) Decisions Trees (DT) were used Two differentstrategies were followed one of them based on the mineral


Table 9 Clusters center of gravity

Variable Cluster 1 Cluster 2 Cluster 3Ag 2433 plusmn 1593 1690 plusmn 1248 1690 plusmn 2309

Al 396503 plusmn 210361 392625 plusmn 226842 227812 plusmn 187093

Ba 1442 plusmn 0962 0996 plusmn 0616 0719 plusmn 0616

Ca 583148 plusmn 246929 617110 plusmn 151838 413341 plusmn 260514

Cd 1316 plusmn 1120 0854 plusmn 0322 0630 plusmn 0526

Cr 1213 plusmn 0933 1753 plusmn 1122 0656 plusmn 0486

Cu 224094 plusmn 168207 238065 plusmn 108827 112320 plusmn 90198

Fe 63880 plusmn 41865 252718 plusmn 74423 36283 plusmn 27911

K 34192173 plusmn 13478898 23008328 plusmn 23670094 21750972 plusmn 12089189

Mg 864049 plusmn 419246 664112 plusmn 268732 533816 plusmn 354098

Mn 64046 plusmn 29957 76347 plusmn 18841 30254 plusmn 19582

Na 1240238 plusmn 624901 1286973 plusmn 354879 598937 plusmn 391228

P 422333 plusmn 167110 219741 plusmn 66174 182971 plusmn 173598

Pb 2440 plusmn 1468 3389 plusmn 1805 1640 plusmn 1256

Zn 72936 plusmn 29664 80972 plusmn 33836 45118 plusmn 29973

Cluster 1

Cluster 2

Cluster 3

Mina S Domingues

MontejuntosSerpaAlmendresS Miguel de MachedeV N S Bento

Beja

Azaruja

－Ｎ Novo

Valverde

（Ｌ＞ da Mitra

（Ｌ＞

da Borralha

６ de R

ocins

３ＮＩ

Aleixo

da Rest

auraccedil

atildeo

Cabeccedila Gorda

BaleizatildeoVilla

nueva del F

resno

Rosal de la

Frontera

N ３Ｌ

de Guadalu

pe

N ３Ｌ

de Mach

ede

voramonte

vora

Meacutertola

Figure 4 Relationships between clusters of mushrooms and different sampling sites

mushrooms content (strategy 1) and the other one based onthe soil mineral composition (strategy 2)

To ensure statistical significance of the attained results25 (twenty-five) runs were applied in all tests the accuracyestimates being achieved using the Holdout method [50] Ineach simulation the available data are randomly divided into

twomutually exclusive partitions the training set with about23 of the available data and used during themodelling phaseand the test set with the remaining examples being used aftertraining in order to compute the accuracy values

TheDT obtained using the strategy 1 is shown in Figure 5Theminerals that contribute to this explanatorymodel are Fe


Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class

lt=0523 mgkg gt0523 mgkg

Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class

Figure 5 The Decision Tree explanatory of segmentation model based in mineral content of mushrooms samples (strategy 1)

Zn and Ba and the rules to assign a case to a cluster are asfollows

(i) If Fe le 11876mgKgand Zn gt 51454mgKgand Ba gt 0523mgKgThenrarr Cluster 1

(ii) If Fe gt 11876mgKgThenrarr Cluster 2

(iii) If Fe le 11876mgKgand Zn le 51454mgKgThenrarr Cluster 3

(iv) If Fe le 11876mgKg

and Zn gt 51454mgKgand Ba le 0523mgKgThenrarr Cluster 3

A common tool for classification analysis is the coincidencematrix [51] a matrix of size 119871 times 119871 where 119871 denotes thenumber of possible classesThismatrix is created bymatchingthe predicted and actual values 119871 was set to 3 (three) in thepresent case Table 10 presents the coincidence matrix Theresults reveal that the model accuracy is 100 both in thetraining set and in test set

In order to relate the mineral mushrooms content to thesoilmineral composition an explanatorymodel of the clustersformedwasmade using the soil content of the sameminerals(ie Fe Zn and Ba) Since the model accuracy is only 74a new explanation model was built up In this attempt all


Table 10 The coincidence matrix for Decision Tree model presented in Figure 5

Variables Training set Test setCluster 1 Cluster 2 Cluster 3 Cluster 1 Cluster 2 Cluster 3

Cluster 1 24 0 0 9 0 0Cluster 2 0 8 0 0 4 0Cluster 3 0 0 14 0 0 10


Variables Training Set Test SetCluster 1 Cluster 2 Cluster 3 Cluster 1 Cluster 2 Cluster 3


inorganic soil mineral content was available The referredmodel is shown in Figure 6 and the respective coincidencematrix is presented in Table 11

The model accuracy was 891 and 826 respectivelyfor training and test sets The soil minerals that contribute tothis explanatory model are Cr Ba and ZnThe rules to assignthe cases to each cluster are as follows

(i) If Cr (soil) gt 3765mgKgThenrarr Cluster 1

(ii) If Cr (soil) le 3765mgKgand Ba (soil) le 3012mgKgand Zn (soil) le 5944mgKgThenrarr Cluster 1

(iii) If Cr (soil) le 3765mgKgand Ba (soil) gt 3012mgKgand Zn (soil) gt 5033mgKgThenrarr Cluster 2

(iv) If Cr (soil) le 3765mgKgand Ba (soil) le 3012mgKgand Zn (soil) gt 5944mgKgThenrarr Cluster 3

(v) If Cr (soil) le 3765mgKgand Ba (soil) gt 3012mgKgand Zn (soil) le 5033mgKgThenrarr Cluster 3

This study observed that the mineral content was influencedby the location area in which the mushroom samples werecollected possibly due to the soil composition and by envi-ronmental factors such as medium temperature vegetationand rainfall Indeed the inorganic composition of A pon-derosa allows group mushrooms according to the locationarea based mainly in their Fe Zn and Ba content On theother hand it is possible to predict the same mushroom

clustering taking into account the mineral soil content basedin Cr Ba and Zn soil composition although with a lowermodel accuracy

Results of mineral composition do not reveal a directcorrelation between inorganic composition of A ponderosamushrooms and their corresponding soil substrate neverthe-less mushrooms are agents that play an important role in thecontinuous changes that occur in their habitats and indeedthey present a very effective mechanism for accumulatingmetals from the environment

Moreno-Rojas et al (2004) in the study of mineral con-tent evaluation of A ponderosa samples from Andalusia alsoverified that variations occurred in the mineral compositionaccording to the sample collection site particularly in relationto Fe K and Na contents Other authors also report thatthe main cause of variation of mineral composition betweensamples of different species of Amanita is the character andcomposition of the substrates (eg sand and wood) and maybe influenced by the presence or absence of ability of thedifferent species for a specific accumulation of some metalsnamely copper and zinc [5] In a study carried out with aspecies of Boletaceae (Suillus grevillei) it is also mentionedthat the variations in mineral composition of the differentsamples are related to the composition of the substrates andthe geochemistry of the soils of each site [11]

4 Conclusions

A ponderosa mushrooms collected from different sitesshowed fruiting bodies with water content of 90ndash93 drymass ranging from 69 to 97 contents of organic matterbetween 62 and 90 and minerals between 05 and 09Mineral composition revealed high content in macroele-ments such as potassium phosphorus and magnesiumCopper chromium iron manganese and zinc essentialmicroelements in biological systems can also be foundin fruiting bodies of A ponderosa within the limits ofRDI Bioconcentration was observed for some macro- andmicroelements such as K Na Cu Zn Mg P Ag Ca andCdThe presence of heavy metals such as barium cadmium


Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg

lt=5944 mgkg gt5944 mgkg lt=5033 mgkg gt5033 mgkg

Figure 6 The Decision Tree explanatory of segmentation model based in mineral content of substrates

lead and silver was quite low within the limits of RDI anddid not constitute a risk to human health

Our results pointed out that it is possible to generatean explanatory model of segmentation performed with databased on the inorganic composition of mushrooms and soilmineral content showing that it may be possible to relatethese two types of data The inorganic analysis providesevidence that mushrooms mineral composition variation isaccording to the collecting location indicating the influenceof the substrate characteristics in the fruiting bodies Therelationship between mineral elements in mushrooms andsoils from the different sampling sites can be an impor-tant contribution to the certification process and seem tobe related to the substrate effects from interindividual orinterstrain differences

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to acknowledge the HIT3CHProject-HERCULES Interface for Technology Transfer andTeaming in Cultural Heritage (ALT20-03-0246-FEDER-000004) MEDUSA Project-Microorganisms MonitoringandMitigation-Developing andUnlockingNovel SustainableApproaches (ALT20-03-0145-FEDER-000015) cofinanced bythe European Regional Development Fund (ERDF) andALENTEJO 2020 (Alentejo Regional Operational Pro-gramme)


References

[1] D Agrahar-Murugkar andG Subbulakshmi ldquoNutritional valueof edible wild mushrooms collected from the Khasi hills ofMeghalayardquo Food Chemistry vol 89 no 4 pp 599ndash603 2005

[2] P G De Pinho B Ribeiro R F Goncalves et al ldquoCorrelationbetween the pattern volatiles and the overall aroma of wildediblemushroomsrdquo Journal of Agricultural and Food Chemistryvol 56 no 5 pp 1704ndash1712 2008

[3] R Zawirska-Wojtasiak M Siwulski S Mildner-Szkudlarz andE Wasowicz ldquoStudies on the aroma of different species andstrains of Pleurotus measured by GCMS sensory analysisand electronic noserdquoACTAScientiarumPolonorumTechnologiaAlimentaria vol 8 no 1 pp 47ndash61 2009

[4] E Guillamon A Garcıa-Lafuente M Lozano et al ldquoEdiblemushrooms role in the prevention of cardiovascular diseasesrdquoFitoterapia vol 81 no 7 pp 715ndash723 2010

[5] J Vetter ldquoMineral composition of basidiomes of AmanitaspeciesrdquoMycological Research vol 109 no 6 pp 746ndash750 2005

[6] P K Ouzouni P G Veltsistas E K Paleologos and K ARiganakos ldquoDetermination of metal content in wild ediblemushroom species from regions of Greecerdquo Journal of FoodComposition and Analysis vol 20 no 6 pp 480ndash486 2007

[7] S Beluhan and A Ranogajec ldquoChemical composition and non-volatile components of Croatian wild edible mushroomsrdquo FoodChemistry vol 124 no 3 pp 1076ndash1082 2011

[8] F S Reis L Barros A Martins and I C F R FerreiraldquoChemical composition andnutritional value of themostwidelyappreciated cultivated mushrooms an inter-species compara-tive studyrdquo Food and Chemical Toxicology vol 50 no 2 pp 191ndash197 2012

[9] X-M Wang J Zhang T Li Y-Z Wang and H-G LiuldquoContent and bioaccumulation of nine mineral elements in tenmushroom species of the genus boletusrdquo Journal of AnalyticalMethods in Chemistry vol 2015 Article ID 165412 2015

[10] J Falandysz M Drewnowska M Chudzinska and DBarałkiewicz ldquoAccumulation and distribution of metallicelements and metalloids in edible Amanita fulva mushroomsrdquoEcotoxicology and Environmental Safety vol 137 pp 265ndash2712017

[11] K Chudzynski and J Falandysz ldquoMultivariate analysis ofelements content of Larch Bolete (Suillus grevillei) mushroomrdquoChemosphere vol 73 no 8 pp 1230ndash1239 2008

[12] H Genccelep Y Uzun Y Tuncturk and K Demirel ldquoDetermi-nation of mineral contents of wild-grown edible mushroomsrdquoFood Chemistry vol 113 no 4 pp 1033ndash1036 2009

[13] J Borovicka Z Randa E Jelınek P Kotrba and C E DunnldquoHyperaccumulation of silver by Amanita strobiliformis andrelated species of the section Lepidellardquo Mycological Researchvol 111 no 11 pp 1339ndash1344 2007

[14] J Borovicka and Z Randa ldquoDistribution of iron cobalt zincand selenium inmacrofungirdquoMycological Progress vol 6 no 4pp 249ndash259 2007

[15] P Kalac ldquoTrace element contents in European species of wildgrowing ediblemushrooms a review for the period 2000ndash2009rdquoFood Chemistry vol 122 no 1 pp 2ndash15 2010

[16] J Falandysz and M Drewnowska ldquoDistribution of mercury inAmanita fulva (Schaeff) Secr mushrooms Accumulation lossin cooking anddietary intakerdquoEcotoxicology andEnvironmentalSafety vol 115 pp 49ndash54 2015

[17] J Falandysz ldquoMercury accumulation of three Lactarius mush-room speciesrdquo Food Chemistry vol 214 pp 96ndash101 2017

[18] C Salvador M R Martins H Vicente J Neves J M Arteiroand A T Caldeira ldquoModelling molecular and inorganic dataof Amanita ponderosa mushrooms using artificial neural net-worksrdquo Agroforestry Systems vol 87 no 2 pp 295ndash302 2013

[19] V Gonzalez F Arenal G Platas F Esteve-Raventos and FPelaez ldquoMolecular typing of Spanish species of Amanita byrestriction analysis of the ITS region of the DNArdquo MycologicalResearch vol 106 no 8 pp 903ndash910 2002

[20] G Moreno G Platas F Pelaez et al ldquoMolecular phylogeneticanalysis shows that Amanita ponderosa and A curtipes aredistinct speciesrdquo Mycological Progress vol 7 no 1 pp 41ndash472008

[21] A T Caldeira C Salvador F Pinto J M Arteiro and MR Martins ldquoMSP-PCR and RAPD molecular biomarkers tocharacterize Amanita ponderosamushroomsrdquoAnnals ofMicro-biology vol 59 no 3 pp 629ndash634 2009

[22] M R Martins C Salvador H Vicente J Neves J M Arteiroand A T Caldeira A Data Mining approach to characterizeAmanita ponderosa mushrooms using inorganic profile andM13-PCR molecular Data In Vasco Cadavez amp Daniel ThielEds Eurosis ndash ETI Publication Ghent Belgium FOOD-SIMrsquo2010 5ndash12 2010

[23] C Salvador M R Martins J M Arteiro and A T CaldeiraldquoMolecular evaluation of some Amanita ponderosa and thefungal strains living in association with thesemushrooms in thesouthwestern Iberian Peninsulardquo Annals of Microbiology vol64 no 3 pp 1179ndash1187 2014

[24] A T Caldeira C Salvador F Pinto J M Arteiro and MR Martins ldquoMolecular biomarkers to characterize Amanitaponderosa mushroomsrdquo in Proceedings of the 10th InternationalChemical andBiological Engineering Conference pp 2094ndash2099Braga Portugal 2008

[25] A Teresa Caldeira J M Arteiro J C Roseiro J Nevesand H Vicente ldquoAn artificial intelligence approach to Bacillusamyloliquefaciens CCMI 1051 cultures Application to the pro-duction of anti-fungal compoundsrdquoBioresource Technology vol102 no 2 pp 1496ndash1502 2011

[26] A T Caldeira J C Roseiro J M Arteiro J Neves and HVicente ldquoProduction of bioactive compounds against woodcontaminant fungi an artificial intelligence approachrdquo inMin-imizing the Environmental Impact of the Forest Products Indus-tries 137 p 131 University Fernando Pessoa Edition Portugal2011

[27] G Malencon and R Heim ldquoNotes critiques sur quelqueshymΦnomycetes dEurope et dAfrique du Nord I Les aman-ites blanches meridionalesrdquo Bulletin trimestriel de la Societemycologique de France vol 58 pp 14ndash34 1942

[28] R Moreno-Rojas M A Dıaz-Valverde B M Arroyo T JGonzalez and C J B Capote ldquoMineral content of gurumelo(Amanita ponderosa)rdquo Food Chemistry vol 85 no 3 pp 325ndash330 2004

[29] A E Greenberg and A D Eaton Standard Methods for theExamination ofWater andWastewater American Public HealthAssociation Washington Wash USA 1992

[30] S M V Fernandes A O S S Rangel and J L F C Lima ldquoFlowInjection Determination of Sodium Potassium Calcium andMagnesium in Beer by Flame Emission and Atomic AbsorptionSpectrometryrdquo Journal of Agricultural and Food Chemistry vol45 no 4 pp 1269ndash1272 1997

[31] F Okumura E T G Cavalheiro and J A Nobrega ldquoSimpleflame photometric experiments to teach principles of atomic


spectrometry in undergraduate analytical chemistry coursesrdquoQuımica Nova vol 27 no 5 pp 832ndash836 2004

[32] AOAC (Association of Official Analytical Chemist) Officialmethods of analysis 15th ed 2nd (suppl 99125) 101ndash102 1991

[33] J MacQueen ldquoSome methods for classification and analysis ofmultivariate observationsrdquo in Proceedings of the 5th BerkeleySymposium on Mathematical Statistics and Probability p 14University of California Press Berkeley Calif USA 1967

[34] M Hall E Frank G Holmes B Pfahringer P Reutemann andI H Witten ldquoThe WEKA data mining software an updaterdquoACM SIGKDD Explorations Newsletter vol 11 no 1 pp 10ndash182009

[35] I H Witten E Frank and M A Hall Data Mining - PracticalMachine Learning Tools and Techniques Elsevier BurlingtonUSA 3rd edition 2011

[36] P Kalac and L Svoboda ldquoA review of trace element concentra-tions in edible mushroomsrdquo Food Chemistry vol 69 no 3 pp273ndash281 2000

[37] M Rudawska and T Leski ldquoMacro- andmicroelement contentsin fruiting bodies of wild mushrooms from the Notecka forestin west-central Polandrdquo Food Chemistry vol 92 no 3 pp 499ndash506 2005

[38] E Sesli M Tuzen and M Soylak ldquoEvaluation of trace metalcontents of somewild ediblemushrooms fromBlack sea regionTurkeyrdquo Journal of Hazardous Materials vol 160 no 2-3 pp462ndash467 2008

[39] European Commission European Commission Opinion of theScientific Committee on Food on the Tolerable Upper IntakeLevel of Copper Health and Consumer Protection Directorate-General Brussels Belgium 2003

[40] FAOWHO Human vitamin and mineral requirements WorldHealth Organization Food and Agriculture Organization ofUnited Nations Rome Italy 2002

[41] E Sesli and M Tuzen ldquoLevels of trace elements in the fruitingbodies of macrofungi growing in the East Black Sea region ofTurkeyrdquo Food Chemistry vol 65 no 4 pp 453ndash460 1999

[42] D Afzali A Mostafavi M A Taher and A MoradianldquoFlame atomic absorption spectrometry determination of traceamounts of copper after separation and preconcentrationonto TDMBAC-treated analcime pyrocatechol-immobilizedrdquoTalanta vol 71 no 2 pp 971ndash975 2007

[43] S Soriano A D P Netto and R J Cassella ldquoDetermination ofCu Fe Mn and Zn by flame atomic absorption spectrometryin multivitaminmultimineral dosage forms or tablets after anacidic extractionrdquo Journal of Pharmaceutical and BiomedicalAnalysis vol 43 no 1 pp 304ndash310 2007

[44] N Abbaspour RHurrell andR Kelishadi ldquoReview on iron andits importance for human healthrdquo Journal of Research inMedicalSciences vol 19 no 2 pp 164ndash174 2014

[45] D Mendil O D Uluozlu E Hasdemir and A Caglar ldquoDeter-mination of trace elements on some wild edible mushroomsamples from Kastamonu Turkeyrdquo Food Chemistry vol 88 no2 pp 281ndash285 2004

[46] G B Gerber A Leonard and P Hantson ldquoCarcinogenicitymutagenicity and teratogenicity of manganese compoundsrdquoCritical Review in OncologyHematology vol 42 no 1 pp 25ndash34 2002

[47] M Tuzen E Sesli and M Soylak ldquoTrace element levels ofmushroom species from East Black Sea region of Turkeyrdquo FoodControl vol 18 no 7 pp 806ndash810 2007

[48] J F Ma P R Ryan and E Delhaize ldquoAluminium tolerance inplants and the complexing role of organic acidsrdquoTrends in PlantScience vol 6 no 6 pp 273ndash278 2001

[49] LCocchi LVescovi L E Petrini andO Petrini ldquoHeavymetalsin edible mushrooms in Italyrdquo Food Chemistry vol 98 no 2 pp277ndash284 2006

[50] J Souza and N Japkowicz ldquoEvaluating data mining modelsa pattern languagerdquo in Proceedings of the 9th Conference onPattern Language of Programs pp 11ndash23 2002

[51] F Provost and R Kohavi ldquoGuest editorsrsquo introduction Onapplied research in machine learningrdquo Machine Learning vol30 no 2-3 pp 127ndash132 1998

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018


International Journal ofInternational Journal ofPhotoenergy


Journal of

Chemistry


Advances inPhysical Chemistry

Hindawiwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2018

Bioinorganic Chemistry and ApplicationsHindawiwwwhindawicom Volume 2018

SpectroscopyInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Medicinal ChemistryInternational Journal of


NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Applied ChemistryJournal of



Biochemistry Research International


Enzyme Research


Journal of

SpectroscopyAnalytical ChemistryInternational Journal of


MaterialsJournal of



BioMed Research International Electrochemistry

International Journal of


Na

nom

ate

ria

ls


Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom


38∘35

01

7

∘51

46

Figure 1 Geographic representation of sampling location areas of the A ponderosa fruiting bodies of forest area of the Alentejo Andalusiaand Extremadura regions In detail we observe the sampling site of Evora region (Alentejo Portugal) showing the surrounding vegetationof Quercus suber Cistus ladanifer and a sample of the fruiting body collected at this local

which they are inserted [11 12] Some minerals are essentialelements for the correct human body function althoughothers may present toxicity [5 6 9 11 13 14] There aremany species of edible mushrooms growing wild and someof them are slightly characterized about minerals contentand the potential of heavy metals bioaccumulation such aslead mercury cadmium and silver [5 13 15ndash17] On theother hand due to the great diversity of wild mushrooms thesimilarity between some species and the lack of knowledge ofothers may lead to intoxication when harvesting wild speciesleading in some cases to death [18]

The genus Amanita is one of the best known from Agari-cales order and comprises edible and poisonous mushroomsdistributedworldwide occupyingmainly amycorrhizal habi-tat and playing a significant role in forest ecosystems [19 20]This genus includes important species of edible mushroomsas is the case of Amanita ponderosa [18 20ndash24] The southof Portugal due to its Mediterranean characteristics anddiversity of flora is one of the regions of Europewith a greaterpredominance of A ponderosa wild edible mushrooms [23]These robust basidiomata grow during spring in mountedareas with acid soils in forests of holm oaks and cork treeslike Quercus ilex and Quercus suber and with shrubs likeCistus ladanifer Cistus laurifolius and Lavandula stoechasestablishing a symbiotic relationship with them (Figure 1)Gastronomically this species is very relevant not only dueto the traditional consumption in the rural populations butalso due to its commercial value in the gourmet marketshaving high exportation potential in Portugal thus thechemical and mineral characterization of numerous speciesof edible mushrooms for certification purposes and furthercommercialization process becoming of extreme importance

In recent years some artificial intelligence based toolsnamely Data Mining Artificial Neural Networks (ANNs)and Decision Trees (DTs) have been applied for fungalenvironment systems [18 25 26] Data mining tools wereused in latest studies aboutA ponderosamushrooms in orderto establish a bridge of inorganic contents molecular finger-prints and geographical sites In one study a segmentationmodel based on the molecular analysis was developed whichallowed relating the clusters obtained to the geographicalsite of sampling There were also developed explanatorymodels of the segmentation using Decision Trees to relatethe molecular and inorganic data following two differentstrategies one based on DNA profile and another based onthemineral composition [22] Another study based on ANNsexposed the selectedmodel and can predict molecular profilebased on inorganic composition with a good match betweenthe observed and predicted values [18]

The aim of this study was to determine the inorganiccomposition of fruiting bodies of A ponderosa mushroomsfrom different sampling sites in the southwest of the IberianPeninsula analysing the variations in mineral content Addi-tionally the mineral composition of the corresponding soilsubstrates was analysed in order to correlate with the mineralcomposition of the fruiting bodies In the present work thek-means clustering method was used to study the mineralcomposition of A ponderosa fruiting bodies To explain thesegmentationmodel that is in order to obtain rules to assigna case to a cluster DTs were used

2 Materials and Methods

21 Sampling Collection Fruiting bodies of the Amanitaponderosa mushrooms were collected in spring between





Alentejo Portugal


Alentejo Portugal















23 Data Mining









3 Results






























































































































































































































































001

01

1

10

100

1000

10000

100000

Min

eral

cont

ent (

mg

kg d

w)
























































































Cluster 1

Cluster 2

Cluster 3

Mina S Domingues


Beja

Azaruja

－Ｎ Novo

Valverde

（Ｌ＞ da Mitra

（Ｌ＞

da Borralha

６ de R

ocins

３ＮＩ

Aleixo

da Rest

auraccedil

atildeo

Cabeccedila Gorda

BaleizatildeoVilla

nueva del F

resno

Rosal de la

Frontera

N ３Ｌ

de Guadalu

pe

N ３Ｌ

de Mach

ede

voramonte

vora

Meacutertola







Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class


Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class




























4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls








Alentejo Portugal


Alentejo Portugal















23 Data Mining









3 Results






























































































































































































































































001

01

1

10

100

1000

10000

100000

Min

eral

cont

ent (

mg

kg d

w)
























































































Cluster 1

Cluster 2

Cluster 3

Mina S Domingues


Beja

Azaruja

－Ｎ Novo

Valverde

（Ｌ＞ da Mitra

（Ｌ＞

da Borralha

６ de R

ocins

３ＮＩ

Aleixo

da Rest

auraccedil

atildeo

Cabeccedila Gorda

BaleizatildeoVilla

nueva del F

resno

Rosal de la

Frontera

N ３Ｌ

de Guadalu

pe

N ３Ｌ

de Mach

ede

voramonte

vora

Meacutertola







Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class


Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class




























4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls









23 Data Mining









3 Results






























































































































































































































































001

01

1

10

100

1000

10000

100000

Min

eral

cont

ent (

mg

kg d

w)
























































































Cluster 1

Cluster 2

Cluster 3

Mina S Domingues


Beja

Azaruja

－Ｎ Novo

Valverde

（Ｌ＞ da Mitra

（Ｌ＞

da Borralha

６ de R

ocins

３ＮＩ

Aleixo

da Rest

auraccedil

atildeo

Cabeccedila Gorda

BaleizatildeoVilla

nueva del F

resno

Rosal de la

Frontera

N ３Ｌ

de Guadalu

pe

N ３Ｌ

de Mach

ede

voramonte

vora

Meacutertola







Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class


Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class




























4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls






























































































































































































































































001

01

1

10

100

1000

10000

100000

Min

eral

cont

ent (

mg

kg d

w)
























































































Cluster 1

Cluster 2

Cluster 3

Mina S Domingues


Beja

Azaruja

－Ｎ Novo

Valverde

（Ｌ＞ da Mitra

（Ｌ＞

da Borralha

６ de R

ocins

３ＮＩ

Aleixo

da Rest

auraccedil

atildeo

Cabeccedila Gorda

BaleizatildeoVilla

nueva del F

resno

Rosal de la

Frontera

N ３Ｌ

de Guadalu

pe

N ３Ｌ

de Mach

ede

voramonte

vora

Meacutertola







Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class


Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class




























4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





















































































Cluster 1

Cluster 2

Cluster 3

Mina S Domingues


Beja

Azaruja

－Ｎ Novo

Valverde

（Ｌ＞ da Mitra

（Ｌ＞

da Borralha

６ de R

ocins

３ＮＩ

Aleixo

da Rest

auraccedil

atildeo

Cabeccedila Gorda

BaleizatildeoVilla

nueva del F

resno

Rosal de la

Frontera

N ３Ｌ

de Guadalu

pe

N ３Ｌ

de Mach

ede

voramonte

vora

Meacutertola







Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class


Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class




























4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





Fe

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Zn

Cluster 2 0

4Total 28

Cluster 1 24

Cluster 3

n

Node 3Class

Ba

Cluster 2 8

0Total 8

Cluster 1 0

Cluster 3

n

Node 6Class

gt11876 mgkg

gt51454 mgkg

lt=11876 mgkg

lt=51454 mgkg

Cluster 2 0

14Total 38

Cluster 1 24

Cluster 3

n

Node 1Class


Cluster 2 0

10Total 10

Cluster 1 0

Cluster 3

n

Node 2Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

0Total 24

Cluster 1 24

Cluster 3

n

Node 5Class




























4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls






















4 Conclusions



Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





Cr (soil)

Ba (soil)

Zn (soil)Zn (soil)

Cluster 2 8

14Total 46

Cluster 1 24

Cluster 3

n

Node 0Class

Cluster 2 1

0Total 15

Cluster 1 14

Cluster 3

n

Node 8Class

Cluster 2 7

14Total 31

Cluster 1 10

Cluster 3

n

Node 1Class

Cluster 2 7

4Total 13

Cluster 1 2

Cluster 3

n

Node 5Class

Cluster 2 0

10Total 18

Cluster 1 8

Cluster 3

n

Node 2Class

Cluster 2 0

2Total 10

Cluster 1 8

Cluster 3

n

Node 3Class

Cluster 2 0

8Total 8

Cluster 1 0

Cluster 3

n

Node 4Class

Cluster 2 0

4Total 4

Cluster 1 0

Cluster 3

n

Node 6Class

Cluster 2 7

0Total 9

Cluster 1 2

Cluster 3

n

Node 7Class

lt=3765 mgkg

lt=3012 mgkg

gt3765 mgkg

gt3012 mgkg







Acknowledgments



References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





References



























































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls




A Data Mining Approach to Improve Inorganic...

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