X-ray fluorescence and gamma-ray spectrometry combined withmultivariate analysis for topographic studies in agricultural soil

Natara D.B. de Castilhos a, Fábio L. Melquiades b,n, Edivaldo L. Thomaz c,Rodrigo Oliveira Bastos b

a Chemistry Department, Universidade Estadual do Centro-Oeste, 85040-080 Guarapuava, PR, Brazilb Physics Department, Universidade Estadual do Centro-Oeste, 85040-080 Guarapuava, PR, Brazilc Geography Department, Universidade Estadual do Centro-Oeste, 85040-080 Guarapuava, PR, Brazil

H I G H L I G H T S

� Characterization of topographic sequence of two hillslopes from agricultural soil.� Employment of EDXRF and gamma-ray spectrometry data combined with PCA.� The combination of green analytical methodologies with chemometric studies allowed soil differentiation.� The innovative methodology is promising for direct characterization of agricultural catchments.

a r t i c l e i n f o

Article history:Received 17 April 2014Received in revised form23 September 2014Accepted 23 September 2014Available online 15 October 2014

Keywords:Tropical soilGeochemical characterizationGamma-ray spectrometryX-ray fluorescencePrincipal component analysis

a b s t r a c t

Physical and chemical properties of soils play a major role in the evaluation of different geochemicalsignature, soil quality, discrimination of land use type, soil provenance and soil degradation. Theobjectives of the present study are the soil elemental characterization and soil differentiation intopographic sequence and depth, using Energy Dispersive X-Ray Fluorescence (EDXRF) as well asgamma-ray spectrometry data combined with Principal Component Analysis (PCA). The study area is anagricultural region of Boa Vista catchment which is located at Guamiranga municipality, Brazil. PCAanalysis was performed with four different data sets: spectral data from EDXRF, spectral data fromgamma-ray spectrometry, concentration values from EDXRF measurements and concentration valuesfrom gamma-ray spectrometry. All PCAs showed similar results, confirmed by hierarchical clusteranalysis, allowing the data grouping into top, bottom and riparian zone samples, i.e. the samples wereseparated due to its landscape position. The two hillslopes present the same behavior independent of theland use history. There are distinctive and characteristic patterns in the analyzed soil. The methodologiespresented are promising and could be used to infer significant information about the region to bestudied.

& 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Soil characterization in a catchment is an important factor foragriculture and environmental conservation. Soil is not homoge-nous and depends on morphological variables for its classification.Physical and chemical properties of soils play a major role in theevaluation of different geochemical signature, soil quality,

discrimination of land use type, soil provenance and soil degrada-tion (Franz et al., 2013; Melquiades et al., 2013; Hernández et al.,2007).

Elemental composition of soils and sediments are widely used toestablish the origin of heavy metals, evaluate anthropogenic influ-ence and the geochemistry of sediments and soil environments(Walling, 2013; Glasby et al., 2004). Besides the major elements, thetrace elements and natural radionuclides are essential to manyenvironmental studies (Navas et al., 2007).

Among the several analytical techniques used for soil analysis, theones that minimize sample preparation, the time consumed inmeasurement and could be considered green techniques, are moreversatile. It is very common to use non-destructive methodologies

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Applied Radiation and Isotopes

http://dx.doi.org/10.1016/j.apradiso.2014.09.0130969-8043/& 2014 Elsevier Ltd. All rights reserved.

n Corresponding author: Tel.:+55 42 36298129.E-mail addresses: fmelquiades@unicentro.br,

melquiades.fabio@gmail.com (F.L. Melquiades).

Applied Radiation and Isotopes 95 (2015) 63–71

like gamma-ray spectrometry and X-ray spectrometry for soil char-acterization (West et al., 2013; Kuang et al., 2012). Furthermore,because of advances in analytical instrumentation, it is now possibleto generate large data sets that are difficult to evaluate using simpleunivariate statistical methods, especially due to their complexity andto their multivariate nature (Luo, 2006). Consequently, multivariatemethods have been widely applied to investigate and interpret thelarge amounts of data generated by current spectrometric methods(Bagur-González et al., 2009; Dragovic and Onjia, 2006). Therefore,synergies obtained by the simultaneous study of multivariate statis-tics and elemental composition data, allow robust interpretations ingeochemical and geological aspects (Gallego et al., 2013; Sielaff andEinas, 2007).

Recently, some researchers used Energy Dispersive X-Ray Fluor-escence (EDXRF) allied to multivariate statistical methods of analy-sis with different objectives, for example precision agriculture, soilquality parameters determination, soil provenance and soil classi-fication (Barsby et al., 2012; Kaniu et al., 2012; Figueroa-Cisternaet al., 2011; Comero et al., 2011; Wastowski et al., 2010; Ye andWright, 2010; Bramley, 2009; Thomaz and Melquiades, 2009;Herpin et al., 2002; Sena et al., 2002). Also, the use of multivariateanalysis, combined with gamma ray spectroscopy is growing(Charro et al., 2013; Fajkovic et al., 2013; Nenadovic et al., 2012;Okeji et al., 2012, Dragovic and Onjia, 2007; Dragovic and Onjia,2006).

The aim of this study is to describe a methodology to obtaincharacteristic soil patterns combining spectroscopic techniqueswith multivariate analysis for direct characterization of agriculturalcatchments. This will be obtained by (a) the soil elemental char-acterization and (b) its differentiation in topographic sequence anddepth of two hillslopes in a catchment, using EDXRF and gamma-ray spectrometry data combined with PCA analysis.

2. Experimental

2.1. Study area

Boa Vista catchment is located at Guamiranga municipality in theCenter South of Paraná state, Brazil (25109023″S and 50154046″W),

Fig. 1. It has an area of �6 km2 and tobacco planting is the mainculture. The lithology consists of diabase intrusion formed mainly ofpyroxene and calcic plagioclase. The forest cover is Araucária Forestand the soil type is an association of Ferralsols and Nitisols with claytexture located at well-drained hillslope sectors and Gleysols restrictedto the riparian zone (FAO, 2006).

2.2. Sampling and preparation

Soil samples from the two hillsides of the catchment, werecollected. One hillslope was cultivated exclusively with tobaccoculture (Nicotiana tabacum) for more than 30 years. The otherhillslope was occupied with mate (Ilex paraguariensis), oat andtobacco crop. In addition, a reference profile at the summit positionfrom a native forest was sampled.

From hillslopes H1 and H2 were collected 8 and 6 samplesrespectively, each of which was composed of 5 representative sub-samples. It was studied over 5 depths: 0–5, 0–10, 10–20, 20–30 and30–40 cm. Metal rings of 100 cm3 were used for 0–5 samplescollection. The other depths were collected using a conventionalauger with 1 m length and with a scoop of 10 cm. Cross contamina-tion was avoided by cleaning the auger and the border of the holebefore each collection. Each sample was composed of 5 subsamplescollected at the same altitude along the hillslope. Therefore, 70samples were collected from the two hillslopes. From the forestreference profile, 9 more samples were collected, at the followingdepths: 0–5, 0–10, 10–20, 20–30, 30–40, 40–50, 50–60, 60–70 and70–80 cm. The altitude and codes of samples are detailed in Table 1.

Samples were dried at 60 1C for 48 h, ground with a mortar andsieved for grain size smaller than 125 mm for EDXRF and 1 mm forgamma-ray spectrometry.

2.3. Instrumentation

EDXRF analysis was performed using the EDX-700 (ShimadzuInc.) in two measurement conditions. The first condition was50 kV, 1 mA, air atmosphere, 100 s and without filters. In this casethe spectra present explicitly the scattering peaks. The secondcondition was used for elements concentration determination. So,a routine containing two ranges of elements were established15 keV, 184 μA, 200 s and Ti filter for Na–Sc and 50 keV, 250 μA,100 s and Zr filter for Ti–U range. Three grams of sample in powderform were placed in XRF cells covered with Mylar film. Each cellwas measured 3 times. For quantitative evaluation, calibrationcurves were constructed with the following certified referencematerials: NIST-1632, NIST-2702, CRM-008, CRM-029, RTC-408,SEM-1646-a, SRM-2711, NRCC-HISS-1, NRCC-MESS-2, CANMET-SO-2, IAEA-04, IPT-42, IPT-51, IPT-57, IPT-63 and IPT-134.

The radionuclides analysis, K, Th and U, was conducted employinga Na(Tl) scintillation detector (76 mm�152mm), model GammaRad5(Amptek Inc.) with 8 cm of Pb shield. The samples were placed inacrylic recipients containing around 180 g of soil and sealed for 30days to reach secular equilibrium. The measurement time was86,400 s. Samples from 0–10 cm to 30–40 cm depths were analyzed.The efficiency calibration was accomplished with the InternationalAtomic Energy Agency (IAEA) RG-Set certified samples, following thecalibration procedure indicated by the same agency. The peaks usedfor the estimates of radionuclides concentrations were 2614 keV ofTl-208 (Th-232 series), 1764 keV of Bi-214 (U-238 series) and 1460 keVof K-40. Since uranium and thorium concentrations were based on theassumption of equilibrium conditions, they were reported as “equiva-lent uranium” (eU) and “equivalent thorium” (eTh) (IAEA-TECDOC-1363, 2003).

The deviations were determined calculating the standard devia-tion from the analysis repetition. Detection and quantification limitswere determined according to Currie (1968).

Fig. 1. Arroio Boa Vista Catchment. The two hillslopes studies are indicated as H1and H2 and the reference forest profile as F.

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–7164

2.4. Principal component analysis

Principal component analysis (PCA) is a well-known unsuper-vised pattern recognition method used mainly to observe thenatural grouping of a data set or for exploratory data analysis(Adam, 1995; Jolliffe, 1986). Typically, PCA is used to reduce thedimensionality of a data set, while retaining as much of the originalinformation as possible. This reduction is achieved by transformingthe original set of variables into a new smaller set, the principal

components (PCs). The first PC accounts for the largest amount ofthe total variance in the original variables. The data interpretationcould be made by scores and loadings plots. The scores representthe position of the sample in the new coordinate system and theloadings describe the variables that contribute to built the new PCaxes from the original variables (Singh et al., 2011; Jolliffe, 1986).

Hierarchical cluster analysis (HCA) was also performed tojustify and validate the formed groups. HCA calculates the distancebetween samples, and the smaller the distances, the more similarthe samples (Brereton, 2002).

Table 1Code samples and altitudes. T¼transect and H¼hillslope.

Samples Sub-samples depth (cm) Land use Altitude (m)

F F_5 (0–5), F_10 (0–10), F_20 (10–20), F_30 (20–30), F_40 (30–40), F_50 (40–50), F_60(50–60), F_70(60–70), F_80 (70–80) Native forest 900T1H1 T1H1_5 (0–5), T1H1_10 (0–10), T1H1_20 (10–20), T1H1_30 (20–30), T1H1_40 (30–40) Mate 870T2H1 T2H1_5 (0–5), T2H1_10 (0–10), T2H1_20 (10–20), T2H1_30 (20–30), T2H1_40 (30–40) Mate 867T3H1 T3H1_5 (0–5), T3H1_10 (0–10), T3H1_20 (10–20), T3H1_30 (20–30), T3H1_40 (30–40) Mate 855T4H1 T4H1_5 (0–5), T4H1_10 (0–10), T4H1_20 (10–20), T4H1_30 (20–30), T4H1_40 (30–40) Mate 848T5H1 T5H1_5 (0–5), T5H1_10 (0–10), T5H1_20 (10–20), T5H1_30 (20–30), T5H1_40 (30–40) Oat 845T6H1 T6H1_5 (0–5), T6H1_10 (0–10), T6H1_20 (10–20), T6H1_30 (20–30), T6H1_40 (30–40) Tobacco 835T7H1 T7H1_5 (0–5), T7H1_10 (0–10), T7H1_20 (10–20), T7H1_30 (20–30), T7H1_40 (30–40) Tobacco 813T8H1 T8H1_5 (0–5), T8H1_10 (0–10), T8H1_20 (10–20), T8H1_30 (20–30), T8H1_40 (30–40) Riparian zone 813T1H2 T1H2_5 (0–5), T1H2_10 (0–10), T1H2_20 (10–20), T1H2_30 (20–30), T1H2_40 (30–40) Tobacco 875T2H2 T2H2_5 (0–5), T2H2_10 (0–10), T2H2_20 (10–20), T2H2_30 (20–30), T2H2_40 (30–40) Tobacco 867T3H2 T3H2_5 (0–5), T3H2_10 (0–10), T3H2_20 (10–20), T3H2_30 (20–30), T3H2_40 (30–40) Tobacco 857T4H2 T4H2_5 (0–5), T4H2_10 (0–10), T4H2_20 (10–20), T4H2_30 (20–30), T4H2_40 (30–40) Tobacco 845T5H2 T5H2_5 (0–5), T5H2_10 (0–10), T5H2_20 (10–20), T5H2_30 (20–30), T5H2_40 (30–40) Tobacco 833T6H2 T6H2_5 (0–5), T6H2_10 (0–10), T6H2_20 (10–20), T6H2_30 (20–30), T6H2_40 (30–40) Riparian zone 816

Fig. 2. (a) EDXRF spectra from 79 samples used in PCA analysis. (b) Score plot PC1versus PC2 for EDXRF spectral data in the range of 10–40 keV.

Fig. 3. (a) Gamma-ray spectrometry overlapping spectra, with highlight in theregion of interest. (b) Score plot of PC1 versus PC3 in the range of 270–3000 keV.

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–71 65

Table 2Concentration values for Al, Fe and Ti determined by EDXRF according to hillslope, position and depth, with its respective standard variation.

Sample Al (%) Fe (%) Ti (%)

F_5 11.8070.69 8.6470.15 1.2670.06F_10 12.3070.63 8.9770.10 1.3470.03F_20 14.5070.19 9.0370.05 1.3870.07F_30 14.6070.16 9.4070.14 1.4270.07F_40 13.8070.33 9.2870.07 1.3770.02F_50 14.9070.28 9.5670.08 1.3770.02F_60 15.4070.75 9.6270.07 1.4170.06F_70 14.5070.35 9.1670.04 1.2770.02F_80 15.5070.42 9.7870.16 1.4070.03T1H1_5 11.0070.10 17.1370.16 1.8270.11T1H1_10 11.8070.30 17.6070.11 1.9570.04T1H1_20 11.4070.44 17.5470.14 1.8770.06T1H1_30 12.8070.59 18.0970.09 1.8970.05T1H1_40 13.0070.58 18.0870.21 2.0170.07T2H1_5 11.0070.50 15.5170.09 1.8670.05T2H1_10 12.0070.40 17.8370.04 1.9670.06T2H1_20 12.270.31 17.8470.06 1.9070.02T2H1_30 11.6070.31 18.0170.15 1.7970.08T2H1_40 12.2070.31 18.3070.19 2.0370.05T3H1_5 10.6070.42 13.5670.07 1.5870.03T3H1_10 10.4070.20 16.7870.20 1.7170.06T3H1_20 11.8070.46 15.9770.15 1.9470.05T3H1_30 11.4070.23 16.4070.03 1.7670.03T3H1_40 11.8070.31 16.5370.06 1.7070.04T4H1_5 12.8070.42 8.8570.12 1.0870.01T4H1_10 12.4070.20 9.3270.13 1.1270.02T4H1_20 13.8070.31 9.7870.05 1.1270.06T4H1_30 13.8070.12 9.6970.10 1.0970.04T4H1_40 14.2070.31 10.0870.03 1.2270.03T5H1_5 11.8070.20 10.2270.06 1.4870.01T5H1_10 12.4070.35 9.9070.19 1.3570.02T5H1_20 12.8070.31 10.1170.04 1.3770.03T5H1_30 12.6070.35 10.1370.05 1.3270.03T5H1_40 14.2070.81 10.3770.04 1.3970.03T6H1_5 11.8071.03 9.2470.12 1.6270.07T6H1_10 11.8070.58 9.2970.20 1.5070.08T6H1_20 12.2070.31 9.8270.25 1.5970.05T6H1_30 13.0070.90 10.0670.09 1.5670.05T6H1_40 13.2070.53 10.1470.07 1.5570.05T7H1_5 7.0070.31 1.7870.03 1.2870.01T7H1_10 7.2070.72 1.8270.02 1.3170.02T7H1_20 7.2070.46 1.8370.05 1.3370.04T7H1_30 8.2070.46 1.8770.01 1.4670.03T7H1_40 9.6070.50 2.0470.06 1.6770.05T8H1_5 6.6070.35 6.0870.09 1.3170.01T8H1_10 7.0070.76 6.2970.07 1.2370.02T8H1_20 8.2071.06 6.1370.02 1.3670.07T8H1_30 7.8070.35 5.8370.04 1.3370.03T8H1_40 8.4070.50 6.0170.05 1.3670.03T1H2_5 11.0070.42 16.7270.16 1.5670.09T1H2_10 10.0070.20 17.1070.14 1.6770.05T1H2_20 10.8070.23 17.2170.11 1.7070.10T1H2_30 11.6070.35 18.0470.08 1.9670.03T1H2_40 11.4070.31 17.3370.10 1.7170.05T2H2_5 10.0072.39 16.6470.12 1.8070.08T2H2_10 9.6070.12 17.2570.08 1.6870.05T2H2_20 10.4070.31 17.5470.15 1.6370.05T2H2_30 11.2070.23 17.9070.10 1.7970.09T2H2_40 11.8070.95 17.9970.18 1.6170.06T3H2_5 10.8070.31 15.2570.14 2.0270.08T3H2_10 11.2070.35 16.5870.13 2.1170.01T3H2_20 9.8070.70 16.5570.11 2.0670.04T3H2_30 11.4070.42 17.4770.11 1.7570.06T4H2_5 11.6070.23 17.8270.05 2.3870.02T4H2_10 10.2070.31 17.9870.14 2.1270.01T4H2_20 10.4070.20 18.2070.08 2.3270.09T4H2_30 10.4070.31 17.8870.21 2.0170.05T4H2_40 11.6070.01 18.5870.05 2.2870.10T5H2_5 11.0070.99 15.3470.11 2.2170.05T5H2_10 11.2070.12 16.0870.06 2.1870.02T5H2_20 11.2070.23 16.4270.03 2.1570.10T5H2_30 12.2070.61 16.3070.04 2.1570.06T5H2_40 11.6070.50 16.7270.09 2.2370.07T6H2_5 7.8070.50 8.3270.01 1.1770.02T6H2_10 8.4070.69 8.3170.05 1.2070.05

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–7166

PCA and HCA analyses were performed with four different datasets: (a) spectral data from EDXRF, (b) spectral data from gamma-rayspectrometry, (c) concentration values from EDXRF measurementsand (d) concentration values from gamma-ray spectrometry.

With EDXRF spectral data a matrix of (79�2048) was constructed,consisting of 79 samples and 2048 counting values related to theenergy channel bin number. For gamma-ray spectrometry 31 sampleswere measured from 0–10 to 30–40 cm depth, and an extra point of70–80 cm from the forest, constructing a (31�1024) matrix. A meancenter preprocessing step was applied for spectral data evaluation.

In addition, two sets of quantitative data were used for PCAanalysis. A matrix of (79�6) corresponding to the Al, K, Ca, Ti, Mn,and Fe concentration values and a (31�3) matrix of eU, eTh and Kconcentrations. For the PCA constructed with the concentration values,auto-scale preprocessing was used and the results were displayed inbi-plots. The visualization of the scores and loadings through bi-plotsmakes possible a better understanding of the system behavior.

The criteria for selecting the number of PCs were based on thescree plot, where the first 3 PCs were shown to be significant withthe remaining components considered to only contribute withnoise to the overall data variance. Detailed information of eachPCA model is presented in Table SM1 in Supplementary material.HCA was performed using the agglomerative Ward's method withMahalanobis distance. Multivariate calculations were performedwith the aid of the software Matlab with PLS ToolBox version 5.8.

3. Results and discussion

3.1. EDXRF spectral data

EDXRF measurements in air atmosphere and without filters onthe X-ray tube were used for PCA analysis of the spectral data(Fig. 2a). The first model performed was a PCA using the complete

Table 2 (continued )

Sample Al (%) Fe (%) Ti (%)

T6H2_20 8.0070.50 4.9470.12 1.3570.09T6H2_30 6.8070.61 4.3570.02 1.3770.03T6H2_40 7.4070.12 4.0670.05 1.4770.01

Fig. 4. (a) Bi-plot of PC1 versus PC2 for EDXRF concentration values. (b) Cluster analysis of EDXRF concentration values for 6 elements (Al, Fe, Ca, Mn, Ti and K).

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–71 67

spectra (0–40 keV) inwhich PC1� PC2 explained 99.91% of the totalvariances (Fig. SM1 in Supplementary material). As was expected,the loadings graph highlights the Fe peak as the most relevant inthe samples distribution due to its high intensity compared to theothers. Score plot allows sample separation in three groups: fromthe riparian zone, the bottom and top of each hillslope. Samplesfrom T1H1 to T4H1 and from T1H2 to T5H2 were displayed in theright side and have higher Fe content. So, Fe peaks differentiate thesamples geographically located at the summit of the hillslope fromthe bottom samples. The forest samples were displayed togetherwith bottom and riparian zone samples, indicating that these soilhave different composition from the top of the transect.

As the Fe peak is a great contribution in the spectra data, thespectral data was studied in a region from 10 to 40 keV, excludingthe Fe peak and emphasizing the scattering peaks. When thepercentage of light elements (Zo11, for example H, N, C, O) ishigh, the X-ray scattering increases, contributing to the formationof Rayleigh, Compton and Raman X-ray scattering. The scatteringpeaks carry implicit information and in this case can be relatedwith organic matter content. The implicit relevant information canbe extracted with the use of multivariate statistics methods ofanalysis, allowing to classify, to correlate and to infer causes forcertain samples characteristics (Melquiades and Santos, in press;Bueno et al., 2005). This new PCA model with scattering peaks,describing 88.10% of the data variance is presented in Fig. 2bwhere the dashed ellipses highlight the forest samples. This scoreplot enables the separation into top and bottom groups, accordingto the ellipses drawn in the graph. The bottom samples, T4H1,T5H1, T6H1, T6H2, T7H1, T8H1 and F could be considered to havehigher organic matter content, which are displayed in theright side of the score plot, in coincidence with the scatteringpeaks region as presented by the loadings plot (Fig. SM2 inSupplementary material). Samples from the top of the catchmentare displayed in the left side of the score plot. The forest profile isconsidered a preserved soil and the riparian zone samples aregrouped to these samples, showing some similarity.

PCA with EDXRF spectral data allows to differentiate samplescomposition from upslope to downslope of each transect, showingthat there is similar composition of the top samples from bothsides of the catchment, and the same behavior from the bottomsamples, which are more similar with the forest reference transect.The methodology is simple, non-destructive and discards manip-ulation of the spectrum.

3.2. Gamma-ray spectrometry spectral data

Fig. 3a shows the spectra obtained from gamma-ray spectro-metry, with the main peaks identified. Score plot from PC1� PC3,explains 94.44% of the total variance (Fig. 3b), shows three groups:the left side correspond to the samples from the top of thehillslopes (T1H1, T1H2, T2H1, T2H2, T3H1, T3H2, T4H2 andT5H2); samples T8H1 and T6H2 are from the riparian zone andT7H1 is at the same altitude level as T8H1; in the downrightquadrant are displayed samples from the bottom, which aresimilar to the forest samples. Also the points of the forest thatare far away from the others refer to 30–40 and 70–80 cm depth,i.e. the agricultural soils from the bottom are similar to the surfacesoil of the forest. The loadings plot shows that PC1 is related withthe Bi-214 (eU) peak and PC3 with the K peak. Potassium was of agreat importance for grouping the samples, especially riparianzone samples, in which K concentration is higher. The bottomsamples have higher U-238 levels than the top ones as they aredisplayed in the positive direction of PC1 (details in Fig. SM3 inSupplementary material).

3.3. Comparative discussion

EDXRF or gamma-ray spectrometry spectral data combined withPCA are advantageous to observe natural grouping on the samplesbecause it is non-destructive, direct, fast and capable of multipledata interpretation without previous information of the studyregion. Besides, it is not necessary to know the concentration valuesof the elements, eliminating spectral manipulation for net areadetermination, equipment calibration and so on.

Both methodologies present the same result, separating thesamples from the riparian zone, the top and the bottom of eachhillslope, i.e. the samples were separated due to their landscapeposition. Also, comparing with the forest sample, which is con-sidered a conserved soil, the top samples of both hillslopes presentslight differences in elemental composition.

Soil along the hillslope displayed a continuum body trans-formed and maintained by physicochemical and biological pro-cesses. Our results indicated that each hillslope sector could beaffected by different soil processes. At the hilltop a chemicaldepletion occurred caused by leaching (i.e., vertical chemicaltransport) and superficial erosion. In the bottom and riparian zonea chemical enrichment occurred with nutrient and organic mat-ters, indicating a hillslope sector with an accumulation function,since it is located at the low part of the relief and receives materialfrom upslope.

3.4. EDXRF quantitative results

Quantitative results from EDXRF were obtained from calibra-tion curves. The accuracy verification of the curves was performedwith SRM2710 (Montana soil), NRCC-PACS-2 (marine sediment)and CANMET-SO-3 (soil) and the relative variation range was from

Table 3Concentration and standard deviation for K, eU and eTh for soil samples obtainedby gamma-ray spectrometry.

Sample and depth (cm) K (%) eU (ppm) eTh (ppm)

F_10 0.10070.003 2.3470.10 17.2870.44F_40 0.08070.003 3.4870.10 22.1370.44F_80 0.08070.003 3.8270.11 22.4370.45T1H1_10 0.03070.003 1.1770.10 6.3170.42T1H1_40 0.02070.003 1.3170.10 6.7570.41T2H1_10 0.04070.003 0.9370.10 6.8270.41T2H1_40 0.01070.003 1.2970.10 7.5970.42T3H1_10 0.03070.003 1.4170.11 10.8070.44T3H1_40 0.02070.003 1.7470.11 11.2170.45T4H1_10 0.08070.003 3.0670.11 16.8370.43T4H1_40 0.07070.003 3.1370.11 19.0370.44T5H1_10 0.10070.003 3.0870.11 16.1170.43T5H1_40 0.12070.003 3.1970.11 17.9670.45T6H1_10 0.16070.003 2.9970.11 16.4270.45T6H1_40 0.12070.003 3.0970.11 16.7070.44T7H1_10 0.23070.003 2.4770.11 10.1770.42T7H1_40 0.20070.003 3.2170.11 14.0670.42T8H1_10 0.44070.004 2.0170.10 9.4370.42T8H1_40 0.54070.004 2.0470.10 9.1370.42T1H2_10 0.01070.003 0.6270.10 12.5670.43T1H2_40 0.04070.003 0.7970.10 7.8870.41T2H2_10 0.10070.003 0.7370.10 5.4770.41T2H2_40 0.02070.003 0.4970.10 6.6870.40T3H2_10 0.08070.001 1.6370.12 10.1370.53T3H2_40 0.00770.003 1.0170.11 8.9970.47T4H2_10 0.00470.003 1.4970.10 5.1770.40T4H2_40 0.03070.003 0.9470.10 6.6970.40T5H2_10 0.08070.003 1.5070.10 7.7270.42T5H2_40 0.04070.003 1.4670.10 8.3770.41T6H2_10 0.32070.003 2.1970.10 13.2370.42T6H2_40 0.49070.003 2.5170.10 11.9870.41

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1% to 28% depending on the element and concentration values. Itwas possible to quantify, with its respective quantification limits,in mg kg�1: Al (8551), K (285), Ca (195), Ti (181), Mn (364) and Fe(144). Aluminum, Fe and Ti are major constituents of the soil andtheir concentrations are in accordance with the type of the soil inthe catchment. Concentration variations for these elements areshown in Table 2 and in Fig. SM4 in Supplementary material.Generally, the values decrease with altitude. So, the riparian zone(T8H1 and T6H2) has the lowest concentrations. Variations inhillslope 2 are smaller than in hillslope 1, indicating higher soilchemical homogeneity. The accuracy verification and concentra-tions for minor elements are presented in the supplementarymaterial (Tables SM2 and SM3). All the concentration values are inthe range of the expected values, compared to a survey performedin soil and rocks of Paraná state (MINEROPAR, 2005), and nocontaminant element was detected in the EDXRF analysis.

The concentration of Al in the reference profile reaches up to15.5%, higher than in the cultivated areas, which ranged from 6.6%to 14.2%. In general, the concentration is smaller in the surficialsamples. One of the reasons is that the culture absorbs the Al fromsoil, making lower in concentration of this element. On the otherhand, the decomposition by organic acids generates higher alu-minum in the forest. Samples T7H1, T8H1 and T6H2, in the bottomposition of the catchment have the lowest Al concentrations. Also,

Al concentrations show a tendency of sample differentiation bydepth in hillslope 1, whose Al values increase in each transect. Aninversion in this behavior could indicate a disturbance in soildynamics, as attested to by samples T7H1 and T8H1 on the bottomof hillslope 1.

Iron and Ti have similar behaviors. Iron has an important con-tribution in EDXRF spectra (see Fig. 2a), and even in abundant amo-unts, its variations along the transects allows a sample differentiation.Thus, in the reference profile Fe concentration ranges from 8.6% to9.8% and it is similar to the bottom samples of the hillslope H1, exceptfor T7H1 and T8H1 that are sampling points in the riparian zone. Inthe hillslope H2, containing tobacco plants, Fe concentration is morehomogenous, ranging from 15.25% to 18.58% except for T6H2.

Fig. 4a presents the Bi-plot with data from the concentrationsvalues determined by EDXRF, and explains 80.82% of the variance.Samples from the riparian zone have the highest K levels and aregrouped with each other. Samples on the right side are enrichedwith Ca, Ti and Fe and refer to the top samples in the catchment.Negative values on PC2 refer to samples with higher Al levels. Inthis case, the forest samples are similar to the bottom samples inthe catchment and could be considered as more preserved soil.Also the HCA (Fig. 4b) for these data separates the samples in twobig groups and also a smaller one inside with the bottom samplesrelated to the riparian zone, corroborating the results.

Fig. 5. (a) Biplot of PC1 versus PC2 and (b) hierarchical cluster analysis of for radionuclide concentrations obtained by gamma-ray spectrometry for K, eU and eTh.

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–71 69

3.5. Gamma-ray spectrometry quantitative results

Concentrations of K, Th-232 and U-238 were determined in all thesamples. The quantification limits were, 0.004%, 0.26 mg kg�1 and0.03 mg kg�1, respectively. The concentrations values for 0–10 cmand 30–40 cm depth are shown in Table 3 and in Fig. SM5 inSupplementary material.

Samples T8H1 and T6H2 have higher K content and both are fromthe riparian zone. In general, K concentrations increase downwardalong hillslope 1. At hillslope 2, samples from 30 to 40 cm have similarconcentration while surficial samples present a greater variabilityfrom T1H2 to T5H2.

Thorium and U have highest concentrations in the forestreference profile and their concentrations are similar to bottomand riparian zone samples (T4H1 to T8H1 and T6H2).

In general, eU concentration increases downward, in both trans-ects. Points T1H1 to T3H1 and T1H2 to T5H2 present the lower Ucontent, probably because it is carried down with the organic matterto the bottom of the catchment. The dominant dynamic process inthe surface soil is between uranium and humic substances present inorganic matter, as attested by Nenadovic et al. (2012) and Schulz(1995).

Hillslope H1 presents an enrichment of eTh from T1H1 to T6H1and the samples near the river, T7H1 and T8H1, are out of thispattern. In hillslope H2, point T1H2 and T6H2 have the highest eThconcentration, while T2H2 to T5H2 have smaller levels and aresimilar in concentration.

Results from the present study were compared with the onesfrom Nenadovic et al. (2012). Our higher values are smaller thanthe lower values obtained for the studied region in Serbia forU-238, Th-232, and K indicating normal levels and the absence ofcontamination.

Fig. 5a presents a PC1� PC2 bi-plot of K, eTh and eU, represent-ing 96.82% of the data variance which were divided into groupsbased on the HCA plot (Fig. 5b). The samples are displayed in threegroups: top, bottom and riparian zone samples. Samples from thetop of the hillslopes are poorest in radionuclide concentration, asthey are separated on the left side. The forest samples are in thesame plot region as the bottom ones and have the highest Th and Ucontents. Samples from the riparian zone are differentiated by itsmajor K content.

4. Conclusion

The purpose of this study was to suggest two direct methodol-ogies for soil chemical properties characterization and to analyze theconcentration variation in topographic sequence and depth of twohillslopes, using EDXRF and gamma-ray spectrometry data com-bined with PCA. The spectral as well as concentration data obtainedby EDXRF and gamma-ray spectrometry methods were used here.All the PCA provided similar results grouping the collected points inthe catchment into top, bottom and riparian zone samples, corro-borated by HCA. In general, the two hillslopes present the samebehavior, independent of land use history.

It was possible to differentiate one point of collection from theother, in topographic sequence, due to their small variations incompositions. With spectral data, it was not possible to identifysignificant differences by depth in each transect. However, quanti-tative data presented a tendency in grouping shallower and deepersoils in depth.

The reference profile, considered a conserved soil, is similar tothe bottom samples of hillslopes H1 and H2. In all score plots,samples from the top were displayed in the opposite side frombottom samples, showing that they have differences in concentra-tion levels. So, the top samples could be considered to some extent

a more depleted soil in comparison with the bottom ones, both bychemical elements and organic matter.

From this study, it was concluded that there are distinctive andcharacteristic patterns in the analyzed soil. This was obtained bycombining spectroscopic techniques and multivariate analysis. Thegreen analytical methodologies are promising and could be usedto infer significant information about the study catchment withapplications in geochemistry, precision agriculture, soil degrada-tion, environment assessment and forensics.

Acknowledgments

To Dr. Maria Izabel Maretti Silveira Bueno for her collaboration inEDXRF and multivariate analysis, to Valdemir Antoneli and PauloFachin for their support on field work and sampling collecting andto CAPES for financial support.

Appendix A. Supplementary material

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.apradiso.2014.09.013.

References

Adam, M.J., 1995. Chemometrics in Analytical Spectroscopy. The Royal Society ofChemistry, Wolverhanpton.

Bagur-González, M.G., Morales-Ruano, S., López-Chicano, M., 2009. Evaluation ofthe environmental contamination at an abandoned mining site using multi-variate statistical techniques—the Rodalquilar (Southern Spain) mining district.Talanta 80 (1), 377–384.

Barsby, A., McKinley, J.M., Ofterdinger, U., Young, M., Cave, M.R., Wragg, J., 2012.Bioaccessibility of trace elements in soils in Northern Ireland. Sci. Total Environ.433, 398–417.

Bramley, R.G.V., 2009. Lessons from nearly 20 years of precision agricultureresearch, development, and adoption as a guide to its appropriate application.Crop Pasture Sci. 60, 197–217.

Brereton, R.G., 2002. Chemometrics Data Analysis for the Laboratory and ChemicalPlant. John Wiley & Sons, New York.

Bueno, M.I.M.S., Castro, M.T.P., Souza, A.M., Oliveira, E.B.S., Teixeira, A.P., 2005. X-rayscattering processes and chemometrics for differentiating complex samplesusing conventional EDXRF equipment. Chemom. Intell. Lab. Syst. 78, 96–102.

Charro, E., Pardo, R., Pena, V., 2013. Chemometric interpretation of vertical profilesof radionuclides in soils near a Spanish coal-fired power plant. Chemosphere90, 488–496.

Comero, S., Locoro, G., Free, G., Vaccaro, S., Capitano, L., Gawik, B.M., 2011.Characterization of alpine lake sediments using multivariate statistical techni-ques. Chemom. Intell. Lab. Syst. 10, 24–30.

Currie, L.A., 1968. Limits for quantitative detection and quantitative determination.Anal. Chem. 40, 586–593.

Dragovic, S., Onjia, A., 2007. Classification of soil samples according to geographicorigin using gamma-ray spectrometry and pattern recognition methods. Appl.Radiat. Isot. 65, 218–224.

Dragovic, S., Onjia, A., 2006. Classification of soil samples according to theirgeographic origin using gamma-ray spectrometry and principal componentanalysis. J. Environ. Radioact. 89, 150–158.

Fajkovic, H., Mikelic, I.L., Prokic, E., 2013. Vertical distribution of 40K, 232Th, and137Cs mass activities in lake sediment (Vransko Lake, Croatia) and theirrelationship with the source material and sedimentation. J. Radioanal. Nucl.Chem. 295, 2273–2282.

FAO, 2006. World Reference Base for Soil Resources—A Framework for InternationalClassification, Correlation and Communication. Food and Agriculture Organiza-tion of the United Nations, Rome p. 103 (World Soil Resources Reports n.).

Figueroa-Cisterna, J., Bagur-González, M.G., Morales-Ruano, S., Casrrillo-Rosúa, J.,Martín-Peinado, F., 2011. The use of a combined portable X ray fluorescenceand multivariate statistical methods to assess a validated macroscopic rocksamples classification in an ore exploration survey. Talanta 85, 2307–2315.

Franz, C., Makeschin, F., Weib, H., Lorz, C., 2013. Geochemical signature andproperties of sediment sources and alluvial sediments within the Lago Paranoácatchment, Brasilia DF: a study on anthropogenic introduced chemical ele-ments in an urban river basin. Sci. Total Environ. 452–453, 411–420.

Gallego, J.L.R., Ortiz, J.E., Sierra, C., Torres, T., Llamas, J.F., 2013. Multivariate study oftrace element distribution in the geological record of Roñanzas Peat Bog(Asturias, N. Spain). Paleoenvironmental evolution and human activities overthe last 8000calyr BP. Sci. Total Environ. 454–455, 16–29.

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–7170

Glasby, G.P., Szefer, P., Geldon, J., Warzocha, J., 2004. Heavy-metal pollution ofsediments from Szczecin Lagoon and the Gdansk Basin, Poland. Sci. TotalEnviron. 330, 176–186.

Hernández, A.J., Alexis, S.S., Pastor, J., 2007. Soil degradation in the tropical forestsof the Dominican Republic's Pedernales province in relation to heavy metalcontents. Sci. Total Environ. 378, 36–41.

Herpin, U., Cerri, C.C., Carvalho, M.C.S., Markert, B., Enxweiler, J., Friese, K.,Breulmann, G., 2002. Biogeochemical dynamics following land use changefrom to pasture in a humid tropical area (Rondônia, Brazil): a multi-elementapproach by means of XRF-spectroscopy. Sci. Total Environ. 286, 97–109.

Jolliffe, I.T., 1986. Principal Component Analysis. Springer-Verlag, New York p. 271.Kaniu, M.I., Angeyo, K.H., Mwala, A.K., Mangala, M.J., 2012. Direct rapid analysis of

trace bioavailable soil macronutrients by chemometrics-assisted energy dis-persive X-ray fluorescence and scattering spectrometry. Anal. Chim. Acta 729,21–25.

Kuang, B., Mahmood, H.S., Quraishi, M.Z., Hoogmoed, W.B., Mouazen, A.M., Hentent,E.J., 2012. Sensing soil properties in the laboratory, in situ, and on-line: areview. Adv. Agron. 114, 155–223.

Luo, L., 2006. Chemometrics and its applications to x-ray Spectrometry. X-RaySpectrom. 35, 215–225.

Melquiades, F.L., Andreoni, L.F.S., Thomaz, E.L., 2013. Discrimination of land-usetypes in a catchment by energy dispersive X-ray fluorescence and principalcomponent analysis. Appl. Radiat. Isot. 77, 27–31.

Melquiades, F.L., Santos, F.R., 2014. Preliminary results: energy dispersive X-rayfluorescence and partial least square regression for organic matter determinationin soil. Spectrosc. Lett. , http://dx.doi.org/10.1080/00387010.2013.874532 (in press).

MINEROPAR, 2005. Levantamento Geoquímico Multielementar do Estado doParaná, Brasil. Minérios do Paraná S.A., Curitiba.

Navas, A., Walling, D.E., Quine, T., Machin, J., Soto, J., Domenech, S., Lopéz-Vicente,M., 2007. Variability in 137Cs inventories and potential climatic and lithologicalcontrols in central Ebro valley, Spain. J. Radioanal. Nucl. Chem. 74, 331–339.

Nenadovic, S., Nenadovic, M., Kljajevic, L., Vukanac, I., Poznanovic, M., Radosavlje-vic, A.M., Pavlovic, V., 2012. Vertical distribution of natural radionuclides umsoil: assessment of external exposure of population in cultivated and unsis-turbed areas. Sci. Total Environ. 429, 309–316.

Okeji, M.C., Agwu, K.K., Idigo, F.U., 2012. Natural radioactivity in cultivated land inthe vicinity of a phosphate fertilizer plant in Nigeria. Radiat. Phys. Chem. 81,1823–1826.

Schulz, R.K., 1995. Soil chemistry of radionuclides. Health Phys. 11–12, 1317–1324.Sena, M.M., Friguetto, R.T.S., Valarini, P.J., Tokeshi, H., Poppi, R.J., 2002. Discrimina-

tion of management effects on soil parameters by using principal componentanalysis: a multivariate analysis case study. Soil Tillage Res. 67, 171–181.

Sielaff, K., Einas, J.W., 2007. The application of multivariate statistical methods forthe evaluation of soil profiles. J. Soil Sediments 7, 45–52.

Singh, V., Afrawal, H.M., Joshi, G.C., Sudershan, M., Sinha, A.K., 2011. Elementalprofile of agricultural soil by the EDXRF technique and use of the principalcomponent analysis (PCA) method to interpret the complex data. Appl. Radiat.Isot. 69, 969–974.

Thomaz, E.L., Melquiades, F.L., 2009. Discriminação de marcadores de proveniênciade sedimento em bacia rural por meio de EDXRF. Rev. Bras. Geomorfol. 10,95–102.

Walling, D.E., 2013. The evolution of sediment source fingerprint investigations influvial systems. J. Soils Sediments 13, 1658–1675.

Wastowski, A.D., Rosa, G.M., Cherubin, M.R., Rigon, J.P.G., 2010. Caracterização dosníveis de elementos químicos em solo, submetido a diferentes sistemas de usoe manejo, utilizando espectrometria de fluorescência de raios-X por energiadispersiva (EDXRF). Quim. Nova 33, 1449–1452.

West, M., Ellis, A.T., Potts, P.J., Streli, C., Vanhoof, S., Wegrzynek, D., Wobrauschek, P.,2013. Atomic spectrometry update – A review of advances in X-ray fluorescencespectrometry. J. Anal. At. Spectrom. 28, 1544–1590.

Ye, R., Wright, A.L., 2010. Multivariate Analysis of chemical and microbial propertiesin histosols an influenced by land-use types. Soil Tillage Res. 110, 94–100.

N.D.B. de Castilhos et al. / Applied Radiation and Isotopes 95 (2015) 63–71 71