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X-RAY FLUORESCENCE COMBINEDWITH CHEMOMETRICS FOR THECHARACTERIZATION OF GEOLOGICALSAMPLES: A CASE STUDY INSOUTHEASTERN SENEGALA. Traore a , P. M. Ndiaye b , M. Mbaye a , F. Diatta b & A. Wague aa Institute of Applied Nuclear Technology, University of Cheikh AntaDiop , Dakar , Senegalb Department of Geology , University of Cheikh Anta Diop , Dakar ,SenegalAccepted author version posted online: 16 May 2014.Publishedonline: 03 Sep 2014.

To cite this article: A. Traore , P. M. Ndiaye , M. Mbaye , F. Diatta & A. Wague (2014) X-RAYFLUORESCENCE COMBINED WITH CHEMOMETRICS FOR THE CHARACTERIZATION OF GEOLOGICALSAMPLES: A CASE STUDY IN SOUTHEASTERN SENEGAL, Instrumentation Science & Technology, 42:5,593-604, DOI: 10.1080/10739149.2014.915564

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X-RAY FLUORESCENCE COMBINED WITH CHEMOMETRICSFOR THE CHARACTERIZATION OF GEOLOGICAL SAMPLES:A CASE STUDY IN SOUTHEASTERN SENEGAL

A. Traore,1 P. M. Ndiaye,2 M. Mbaye,1 F. Diatta,2 and A. Wague1

1Institute of Applied Nuclear Technology, University of Cheikh Anta Diop, Dakar, Senegal2Department of Geology, University of Cheikh Anta Diop, Dakar, Senegal

& Characterization of magmatic, metamorphic, and mineralized metamorphic rock collected insoutheastern Senegal in West Africa was performed based on elemental analysis by energy dispersiveX-ray fluorescence. The rock classification scheme based on chemical composition was obtained bya combination of principal component analysis and hierarchical clustering techniques to discriminatemagmatic, metamorphic, and mineralized metamorphic rock. An improved sensitivity of signal-to-noise ratio was obtained using a thin silver X-ray excitation source with several appropriate filters.Energy dispersive X-ray fluorescence was used to characterize the major oxides, and the results werecompared to previous studies.

Keywords chemometrics, hierarchical cluster analysis, principal component analysis,rock, X-ray fluorescence

INTRODUCTION

Various methods have been employed for the analysis of silicate rock forthe determination of major and trace elements.[1] X-ray fluorescence (XRF)is used for major elements (Na, Mg, Al, Si, P, K, Ca, Ti, Mn, Fe) and traceelements (Rb, Sr, Y, Nb, Zr, Cr, Ni, Cu, Zn, Ga Pb, Th, U, La, Ce, Nd,Sm).[2–5] Instrumental Neutron activation (INAA) is used for the determi-nation of specific trace elements (Sc, Co, Cr, Cs, Hf, Ta, Th, U) and the rareearth elements (REE) (La, Ce, Nd, Sm, Eu, Tb, Yb, Lu). Alternatively, rareearths may be determined by isotope dilution mass spectrometry. Inductivelycoupled plasma techniques, such as inductively coupled plasma atomic emis-sion spectrometry (ICP-AES), are also employed for geological materials.[6]

Address correspondence to A. Traore, Institute of Applied Nuclear Technology, University ofCheikh Anta Diop. P.O. Box 5005, Dakar, Senegal. E-mail: alassanemeister@gmail.com

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/list.

Instrumentation Science and Technology, 42:593–604, 2014Copyright # Taylor & Francis Group, LLCISSN: 1073-9149 print/1525-6030 onlineDOI: 10.1080/10739149.2014.915564

Instrumentation Science and Technology, 42:593–604, 2014Copyright # Taylor & Francis Group, LLCISSN: 1073-9149 print/1525-6030 onlineDOI: 10.1080/10739149.2014.915564

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The results of these instrumental techniques have been used to attemptto classify the geological materials,[7] specify the nature of the magmaticseries,[8–11] and identify the paleotectonic setting.[12–14]

However, most of the techniques described above require samplepreparation with possibility of errors. To overcome these difficulties, in thiswork, rock was analyzed for major, minor, and trace elements by energydispersive X-ray fluorescence spectroscopy (EDXRF) with chemometrictechniques. Although the multielemental capability and the relatively lowcosts make this technique a useful tool, there is a lack of consensus onthe selection of methods and on the estimation of the uncertainty of theresults.[15] The technique is nevertheless limited by matrix effects, mineralheterogeneity, and the surface irregularity of the sample, factors thathamper the accuracy of the results.[16] Chemometric techniques reducethese interferences[17] through spectral modeling to provide correct for

FIGURE 1 Geological map of Eastern Senegal.

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matrix effects, resolution of intense overlapping spectral lines, and directcharacterization of igneous rocks. The objective of this work is to classifymagmatic, metamorphic, and mineralized metamorphic rock on the basisof elemental concentration. Oxides were characterized by EDXRF andthe results were compared to previous studies.[18]

MATERIAL AND METHODS

Geological Materials

The geological material was derived from eastern Senegal. The recentmapping[19–22,24] of this part of Senegal has distinguished three super-groups (Figure 1). The Birimian supergroup (Paleoproterozoic), illustratedin Figure 1, areas 1, 2, 3, 4, and 5, includes a Mako group, in the westregion, characterized by volcanic formations, magmatic suite, and is domi-nated by granodiorite. Its pillow lavas basalt is associated with conglomer-ates, sandstones, greywacke with andesite, and andesitic breccias. Themagmatic suite consists of several generations of pluton ranging from gab-bro to granite. The Diale-Dalema group at the east is formed by carbonates,greywacke, pelite, sandstone, and quartzite and contains andesites andrhyodacite. This group is also intersected by a magmatic suite that includesgranodiorites, monzongranites, diorites, microdiorites, rhyolites, rhyoda-cites, andesites, and trachytes.

The Segou=Madina-Kouta supergroup outcrops along the borderbetween Senegal and the Republic of Guinea and consists of meso- andneoproterozoic sedimentary rock. The Mauritanides supergroup includesa stratigraphy autochthonous area that corresponds to the Taoudeni Basin.Another domain called parautochthonous is transported oncambro-ordovician sediments that includes groups composed of felsicand sedimentary rock. The allochthonous area is in the northern part;some allochthonous units contain micaschistes, quartzites, sericitic schistes,mylonitic granite, and acid volcanic rocks. Other allochthonous units con-sist of serpentinites often associated with mylonitic quartzites, metabasalts,metagabbros, and ferruginized chloritic-schists.

XRF Analysis

XRF was performed with 100% normalization and full fundamentalparameter quantification techniques. The metal content of sodium wasdetermined by ICP-AES to be 0.40, 4.70, 5.14, and 4.12% in ultrabasite(UB), granodiorite-mylonite (D155), granodiorite (D153), and granite(D141A), respectively.

X-Ray Fluorescence and Rocks Study 595

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Samples were prepared with a flat surface without scratches and stria-tions by polishing and were cut in order to fit into the window of theXRF instrument. For small masses of sample (SAT1, SAT2, and GA), pelletswere made as a mixture of rock powder and ethylene bis-stearamide(known as Licowax or Hoechtwax HWC) as a binding agent in a ratio of1 g of rock sample to 10% of the binding agent. The mixture was thor-oughly homogenized in a planetary mill and pressed under 15 tons into adisc 32mm in diameter. The pellets were analyzed by EDXRF at 50 kVand 40mA max with a silver-anode excitation source and filters as secondarytargets for excitation (Table 1). With this configuration, matrix effects werereduced due to element overlapping and the signal-to-noise ratio wasimproved by sequentially selecting appropriate combinations of filters assecondary targets. Thus, different groups of elements were determineddepending on the target. Three replicate measurements were performedusing an analysis time of 200 s. The accuracy of the instrument was verifiedby analyzing five different standard materials: CCRMP TILL-4 (180-601),NIST 2780 (180-625), SiO2 99.995% (180-472), RCRA (180-436), and NIST2709a (180-469a).

EDXRF SPECTRAL DATA AND CHEMOMETRIC ANALYSIS

Conventional computer techniques that have been used for XRF analy-sis[26] involve searching the spectrum for characteristic peaks, followed byan iterative procedure for matching the fitted peaks and intensities. Withhigh resolution germanium detectors, the photopeaks are well resolvedand the process is relatively simple. However, with more common low res-olution detectors, such as Si (Li),[27] the process is less accurate and moretime consuming. This limits the use of such algorithms[28] for the analysisof complex matrices. To avoid this limitation, the intensities for the rockwere processed using least-squares regression analysis and statistical evalu-ation. The linear equation Y¼ aþbx was used to compute the intensity.[29]

TABLE 1 Spectrometer specifications and operating conditions

Resolution 178 eV at Mn KaWindow Thickness 12.7mm BeRating 50 kV, 40mA maximum power of the tube 2WBeam diameter 7mmFilter Elements analyAg excitation source Sb, Sn, Cd, Pd, Ag, Mo, Nb, Zr, Sr, Rh, Bi, As, Se, Au, Pb,

W, Zn, Cu, Re, Ta, Hf, Ni, Co, Fe, Mn, Cr, V, Ti, Th and USandwich of Al, Ti and Mo Ba, Sb, Sn, Cd, Pd, AgCu Filter Cr, V, Ti, Ca, KNo Filter Al, P, Si, Cl, S, Mg

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Principal component analysis (PCA) is an unsupervised statistical analy-sis technique that finds linear combinations of variables describing majortrends in the data set. PCA provides a useful tool for comparison of sampleswhich variables are responsible for differences. In order to perform thedata processing for rock classification based on chemical composition,the results were characterized by a combination of PCA and hierarchicalclustering techniques. SPSS software was selected as a chemometric toolfor PCA, while Matlab 7.10 (Mathworks Natick) was used for hierarchicalcluster analysis. Mean-centuring and auto-scaling techniques were utilizedfor data pre-processing. Scaling reduces collinearity among the variablesand allows their contribution to the analysis.

RESULTS AND DISCUSSION

Several types of rock were analyzed by energy dispersive X-ray fluore-scence (EDXRF), and the results are recorded in Table 3. The elementsthat are concentrated in most geological samples and whose concentrationsvary largely among the different petrogenesis formations include barium,chromium, iron, manganese, zirconium, and thorium. Scandium wasconcentrated in the ferromagnesian minerals formed during the initialcrystallization of the rocks, as well as in phosphorous limestone and basaltictuff. As some elements are concentrated during the weathering processes

TABLE 2 Analysis of NIST SRM 2780

Element

Certified value�uncertaintymg=kg except whereindicated otherwise Average, mg=kg

Standard deviation,mg=kg

Relative standarddeviation, %

Si 34.1� 0.2% 21.82 0.09 0.41Al 8.87� 0.33% 88007.23 790.64 0.90Cd 12.1� 0.24 11.98 3.18 26.54Pb 5770� 410 5136.42 69.84 1.36As 48.8� 3.3 50.06 5.03 10.05Fe 2.78� 0.08% 27235.71 263.07 0.97K 3.4� 0.3% 34545.16 3096.47 8.96Ca 0.2� 0.02% 2052.61 45.92 2.24P 427� 40 450.37 68.19 15.14Mn 462� 21 449.38 21.12 4.70S 12630� 420 12877.18 203.46 1.58Sr 217� 18 219.85 2.82 1.28Rb 175 182.49 0.3 0.16Cu 215.5� 7.8 237.63 1.3 0.55Zn 2570� 160 2069.4 13.92 0.67Mg 5330� 200 N.D� — —

(�) Not Detected.

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TABLE3

Analysisofge

ologicalsamplesbyEDXRF

Geo

logicalrocks

samples(L

abel)

Elemen

ts

Si(%

)Fe(%

)Mn

Ti

Cr

KCa

As

Zr

SSr

Granite(D

141A

)36

.8�0.2

2.8�0.02

674.1�48

.855

05.6�41

.3ND

1622

0.6�16

3.4

3399

3.9�19

2.8

4.6�1.3

117.8�3.1

356.8�75

.341

1.0�5.0

Granodiorite-m

ylonite(D

142)

34.6�0.13

1.7�0.02

293.2�17

.616

89.1�31

.5ND

7540

.6�99

.921

009�12

38.6�2.3

103.4�2.8

ND

136.2�1.9

Granodiorite

(D15

3)33

.3�0.2

2.9�0.02

566.4�23

.436

68.6�46

.114

.8�7.7

2530

0�30

0012

721.7�20

0.8

13�1.3

49�2.03

695.9�47

.512

6.3�1.9

Granodiorite-m

ylonite(D

155)

39.4�0.2

0.8�0.01

264.8�33

.812

28.1�14

.3ND

2069

4.2�16

9.1

3284

.2�46

.22.8�1.2

62.9�1.5

ND

54.5�1.3

BasalticTuff(D

161)

30.2�0.2

87.6�0.02

1127

.04�34

.913

319�67

1117

.8�11

.526

79.4�78

.196

581.5�22

9.9

28.5�1.4

174.2�2.4

1130

�30

367.2�3.1

Sulfidebearingbreccia

(SAT2)

ND

74.9�0.01

ND

895�48

450.5�12

.5ND

ND

599�9

67.1�2.4

2269

19�13

935.4�1.4

Ferrugineo

us

quartzite(Q

F)

14.4�0.1

37.9�0.09

772.1�10

7.9

415�54

5095

.6�34

.5ND

ND

67.9�3.8

177.0�4.0

303.7�17

7.03

ND

Uraniferoussyen

ite(S)

33.3�0.2

3.9�0.04

820�70

9600

�80

0ND

7525

.7�10

6.6

1129

60.2�26

6.6

20�10

100�10

490�60

467.5�3.6

Phosphorouslimestone(P

)17

.6�0.1

0.16

�0.00

338

.7�12

.683

5�24

26.7�7.9

329.5�73

.717

1047

.8�33

4.9

2.9�1.4

13.6�2.7

1327

.8�15

7.2

719.2�4.8

Sulfidebearingtuff(Sa)

27.9�0.1

33.7�0.1

ND

2846

�45

278�12

403.9�89

.623

04.9�50

137.5�4.03

111.8�2.8

1434

92.0�11

85.0

31�2

Peridotite-m

ylonite(301

)16

,3�0.1

20.2�0.04

2909

.1�66

.886

79.9�91

.861

3.5�15

.314

8.5�97

.828

1.9�61

.654

.3�2.04

103.8�2.3

302.4�16

4.4

ND

Sulfidebearingquartzite(332

)26

,9�0.1

17,7�0.03

3392

.8�52

.647

11.2�47

.629

2.6�7.9

3135

4.5�17

1.4

547.1�61

.471

.8�2.2

188.8�2.3

362.4�93

.732

.1�1.2

Sulfidebearing

quartzite(119

)21

,9�0.1

20,88�0.03

165.9�28

.812

05.2�40

.937

1.8�8.8

5633

.1�73

.766

9.1�36

.666

.7�2.1

46.3�2.8

1053

.1�33

.62.8�1.4

Ferrugineo

usco

ncretion

(#15

1)14

,7�0.06

3.3�0.03

897.2�67

.915

81.1�36

115.9�26

.02

8795

.6�20

3.3

2076

89.8�10

22.2

47.3�4.2

100.7�3.2

2043

9.0�13

2.0

42.6�2.1

Ferrugineo

usco

ncretion

(#15

2)34

,8�0.1

9.36

�0.06

286.9�52

.331

41.9�48

.369

.3�28

.335

38.6�15

6.4

2350

.2�61

.853

.01�3.4

319.8�5.6

436.6�37

.212

.6�1.5

Andesitic

tuff(#

154)

37,4�0.12

1.29

�0.01

392.1�49

.622

00.9�21

.1ND

3093

0.3�29

3.7

6127

.1�69

.15.5�2.5

256.1�4.3

302.3�35

.212

3.4�2.5

Mylonitic

ferrugineo

us

quartzite(317

)49

.4�0.2

4.1�0.03

33.1�9.8

ND

ND

184.2�36

.520

70�40

30�10

ND

128.9�66

.8ND

Sulfidebearingquartzite(216

)41

,7�0.2

0.8�0.01

1360

.1�43

.715

20.3�20

.111

4.8�6.2

5771

.01�56

.613

332.1�65

.315

.14�1.02

28.3�1.03

420.6�66

.211

.8�0.7

Quartzite(267

)26

,7�0.1

8,05

�0.02

280�21

.928

74�62

.114

9�12

.838

00�12

0.5

4460

�99

.026

.2�1.2

32�1.2

246�13

9.0

5.6�1

Geo

logicalrocks

samples(L

abel)

Elemen

ts

PCu

SnU

Zn

Th

ScCs

Te

Ba

Mo

Granite(D

141A

)10

71.8�20

9.6

ND

131.1�6.3

ND

16.4�2.6

ND

8.5�1.8

163.2�14

.552

0.5�15

.812

37.7�34

,83.6�1.6

Granodiorite-m

ylonite(D

142)

568.7�87

.9ND

132.6�6.1

ND

36.6�5.7

ND

9�2

170�14

523.5�15

.247

2.6�21

.6ND

Granodiorite

(D15

3)35

5.8�12

0.3

ND

124.3�6.2

6.8�3.2

31.7�3.2

ND

2.7�1.6

63.1�14

.348

6.4�15

.768

7.5�23

.8ND

Granodiorite-m

ylonite(D

155)

660�60

ND

117.9�5.7

7.3�3.1

75.7�3.6

10.3�2.0

ND

ND

467.9�14

.653

6.6�21

.7ND

BasalticTuff(D

161)

2070

�70

90�20

162.5�6.7

ND

90�10

ND

22.4�3.4

269.5�15

.157

1.3�16

.357

3.1�23

.2ND

Sulfidebearingbreccia

(SAT2)

ND

ND

141.2�9.9

ND

ND

ND

3.8�1.1

ND

456.2�24

.3ND

121�3

Ferrugineo

usquartzite(Q

F)

ND

ND

263.9�16

.3ND

29.3�8.1

ND

3.13

�1.1

1016

�38

1041

.7�40

.514

12.2�56

.6ND

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Uraniferoussyen

ite(S)

850�13

0ND

132.5�6.4

64.4�4.1

70�10

7.2�2.2

ND

113.3�14

.750

6.2�16

.125

3.3�21

.9ND

Phosphorouslimestone(P

)93

600�47

339

.8�12

.413

7.8�7.3

79.04�4.5

499.3�18

.8ND

24.1�4.9

181.7�16

.753

0.3�18

.134

9�25

15.3�2.2

Sulfidebearingtuff(Sa)

ND

20.02�10

.413

4.0�10

.0ND

ND

ND

2.0�1.0

174.5�26

.243

2.4�23

.735

2.03

�38

.926

.2�2.3

Peridotite-m

ylonite(301

)ND

56.8�9.5

138.3�8.2

ND

96.5�9.6

ND

4.4�1.2

140.6�18

.749

6.9�20

.325

3.02

�27

.73.0�

2.1

Sulfidebearingquartzite(332

)ND

20.8�7.4

144.7�6.8

13.7�4.0

19.4�5.5

ND

1.7�0.7

245.9�15

.755

6.1�17

.012

62�28

7.5�1.7

Sulfidebearingquartzite(119

)ND

229.1�7.8

166.8�6.8

ND

37.3�8.3

ND

3.9�01

336.2�15

.864

5.04

�17

.352

7.8�23

.6ND

Ferrugineo

usco

ncretion

(#15

1)19

19.6�83

.317

1.5�14

.7ND

ND

46.5�7.6

ND

ND

ND

ND

ND

ND

Ferrugineo

usco

ncretion

(#15

2)21

45.2�10

5.1

ND

ND

ND

37.6�7.1

ND

ND

ND

ND

ND

ND

Andesitic

tuff(#

154)

1120

.2�10

1.4

ND

ND

ND

34.1�5.9

ND

ND

ND

ND

ND

ND

Mylonitic

ferrugineo

us

quartzite(317

)45

0�11

06.6�2.9

130.9�10

.5ND

ND

ND

ND

138.4�24

.149

9.6�26

.226

0.8�35

.8ND

Sulfidebearingquartzite(216

)32

3�25

0.1

5.9�3.4

137.1�4.9

ND

15.01�3.2

ND

1.3�0.6

221.3�11

.656

9.5�12

.741

0.4�17

.5ND

Quartzite(267

)ND

ND

172.9�7.3

ND

19.8�4

ND

3.2�1.6

401.2�16

.766

6.6�18

.270

8.8�25

.9ND

Geo

logicalrocks

samples(L

abel)

Elemen

ts

Rb

VNi

Al(%

)Pd

Sb

Granite(D

141A

)52

.3�2.1

88.5�4.1

40.3�7.1

7.6�0.1

44,2�5.7

31,1�5.8

Granodiorite-m

ylonite(D

142)

26.6�1.1

29.4�9.7

40.6�6.9

7,4�0.2

41,9�5.4

35,4�5.6

Granodiorite

(D15

3)80

.4�1.8

95.2�13

.937

.6�7.4

8.8�0.4

39,3�5.6

25,6�5.8

Granodiorite-m

ylonite(D

155)

95.8�1.8

16.6�8.2

23.9�6.5

6.5�0.3

41,9�5.3

21,2�5.4

BasalticTuff(D

161)

ND

273.2�18

.737

5.7�11

.78,5�0.2

39,9�5.7

43,5�5.9

Sulfidebearingbreccia

(SAT2)

ND

130�15

.158

5.9�32

.2ND

71,7�9.7

18,8�9.04

Ferrugineo

usquartzite(Q

F)

ND

363.8�19

.514

60.1�36

.11,4�0.1

77,1�13

.819

4,4�14

.9Uraniferoussyen

ite(S)

46.2�1.6

ND

12.2�6.9

6,9�0.1

40,1�5.7

33,9�5.9

Phosphorouslimestone(P

)16

.6�1.9

91�8.0

33.6�8.1

2,5�0.2

45,2�6.5

36,8�6.7

Sulfidebearingtuff(Sa)

ND

98.7�15

.918

2.5�20

.98,2�0.3

44,6�8.9

26,9�10

.3Peridotite-m

ylonite(301

)ND

329.6�27

.113

19�23

8,6�0.2

41,6�7.3

28,6�7.5

Sulfidebearingquartzite(332

)11

1.4�2.3

173.3�14

.526

1.1�11

.44,5�0.1

41�6.0

47,4�6.3

Sulfidebearingquartzite(119

)3.6�1.3

105.4�10

.112

7.9�9.5

0,3�0.07

53,5�6.1

79,5�6.4

Ferrugineo

usco

ncretion

(#15

1)13

4.5�3.9

45.7�5.5

ND

4,9�0.1

ND

ND

Ferrugineo

usco

ncretion

(#15

2)ND

38.3�6.3

ND

8,1�0.2

ND

ND

Andesitic

tuff(#

154)

85.1�2.7

64.7�3.8

ND

7,5�0.2

ND

ND

Mylonitic

ferrugineo

us

quartzite(31

7)ND

45.9�7.3

44.4�4.9

ND

43.4�9.5

21.8�9.6

Sulfidebearingquartzite(216

)7.9�0.6

30.6�6.5

45.7�5.6

0,2�0.2

43,2�4.4

48,4�4.7

Quartzite(267

)1.2�0.9

160.7�19

.572

.9�7.9

6,1�0.2

50.4�6.3

79.9�6.8

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leading to the formation of the rock beds, their concentrations vary. Dueto the absence of helium flush or vacuum in the setup, sodium was notdetected, unless using an ultra-light detector window. Its presence washighly correlated with calcium.

Table 4 shows results obtained by EDXRF and inductively coupled plasma-atomic emission spectroscopy (ICP-AES). There was good agreement betweengranodiorite and diorite. The concentrations of the determined elements werereported as the concentration of the corresponding oxide. SiO2 was signifi-cantly higher in granite but lower in ultrabasite; Al2O3 was lower in dacitesbut higher in basalts; CaO was generally lower in gabbro and ultrabasites.

The PCA procedure was sorted in order of significance, where the firstcomponent was essential for the proper reconstruction of the spectralsignature and the last component contained sample specific superimposedrandom noise. By choosing a truncation value, i.e., a number of spectralcomponents, the information was separated from the noise.[30] Once thedata were autoscaled, PCA was performed convert the data structure to areduced dimension, retaining the maximum amount of variability.[31]

PCA promotes dimension reduction of the original data, modeling of data,detecting of outliers, selection of main variables, classification of samples,and prediction of sample characteristics.[32] The first four extractedcomponents accounted for nearly 92.05% of the variability of the dataset. The highest variability in the data set (74.73%) was accounted forall groups of rock. The running matrix elements used for data processingwithin the samples was Na, Al, Si, P, S, K, Sb, Ca, Te, Sc, Cs, Ti, V, Cr, Mn, Ni,Cu, W, Zn, As, Pb, Th, Rb, U, Sr, Y, Zr, Mo, Pd, and Cd.

In the first loading score, Si, Ca, S, Ti, Ni, Ba, and Zr had good contri-butions in sulfide bearing breccia (AS), peridotite (UB), andesite (AN),and ferruginous quartzite (267). Other metals, Sr, Cu, Zn, Sb, Sc, Cr,and Mn in the second, third and fourth components, also contributed tovariability in the data and the fact that their loading is the same sign mayindicate their association with other minerals.

From the literature,[33] bearing breccia (AS) and ferruginous quartzite(267) are classified as metamorphic rock, while peridotite (UB) and andes-ite (AN) are magmatic rock, illustrating the difficulty in classify rock regard-ing to their petrogenesis on the basis of geochemical content using PCAanalysis. However, PCA is often used in combination with cluster analysis,an unsupervised procedure that allows one to draw a multidimensionalspace of variables in a two-dimensional tree called dendrogram built onthe basis of the similarity between samples (Figure 2). The correlationbetween samples was calculated on the basis of the squared Euclidiandistance, while the Ward method was used to establish the cluster. In thisstage, 26 metal concentrations were determined in the 19 rock samples.To ensure such distances between sample spectra work well, it is worthwhile

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TABLE4

ComparisonofEDXRFan

dIC

P-AESfordeterminationofmajorelem

ents

(%)

Granite

Granodiorite

Diorite

Gab

bro

Orthogn

eiss

Ultrabasite

Andesite

Dacite

Basalte

D14

1AD15

3DIO

B31

1D15

5UB

AN

DA

B25

6B

Major

elem

ents

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

XRF

ICP-AES

SiO

273

.563

.860

.863

.762

.959

.04

54.4

45.7

60.8

71.3

40.2

41.0

55.6

52.0

59.1

60.4

54.4

48.3

Al 2O

321

.915

.615

.615

.310

.213

.612

.314

.012

.014

.44.4

4.9

14.0

13.7

13.1

16.5

19.6

13.9

CaO

3.7

3.9

2.0

2.7

6.8

8.2

7.9

11.5

21.0

1.9

4.8

3.08

9.2

4.1

5.02

10.2

9.8

Cr 2O

3ND

0.04

ND

0.1

0.03

0.04

0.1

0.02

ND

0.1

0.4

0.5

0.04

0,1

0.1

0.1

0.1

0.02

Fe 2O

33.3

4.6

3.4

4.8

6.4

7.1

10.2

16.5

3.4

2.9

17.6

12.5

9.4

10.2

2.1

3.7

11.4

15.9

K2O

4.9

2.6

5.6

2.9

0.7

0.6

0.1

0.2

5.6

2.9

0.1

0.2

0.2

3.3

0.1

0.3

0.1

0.3

MnO

0.1

0.1

0.05

0.1

0.1

0.1

0.1

0.3

0.05

0.2

0.3

0.2

0.1

0.2

ND

0.04

0.1

0.3

P2O

50.1

0.2

ND

0.2

ND

0.1

0.1

0.1

ND

0.1

ND

0.03

0.1

0.3

0.2

0.1

0.1

0.1

TiO

20.2

0.6

0.4

0.6

0.4

0.4

0.4

1.6

0.4

0.4

0.2

0.3

0.7

0.7

0.2

0.4

0.6

1.5

MgO

ND

1.95

ND

2.1

ND

6.1

ND

6.9

ND

3.9

8.4

28.8

ND

7.6

ND

4.5

6.5

6.0

Na 2O

NA��

4.1

NA

5.1

NA

2.9

NA

2.0

NA

4.7

NA

0.3

NA

2.6

NA

7.8

NA

3.3

(�� )

NotAvailab

le.

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that the different dimensions are of comparable magnitudes. One wayto address this is to keep equal the signal-to-noise ratios in each dimension.This result can be understood from right to left as a series of C shapes;the width of each C represents the statistical distance between twosample groups in a multidimensional space (Figure 2). Even if the methodis unsupervised, it was capable of sorting the samples in correct groups incompliance with the description in the literature[33] except four samplesin two groups which relied on the behavior of the complex process ofthe petrogenesis rocks. The performance may be improved by choosinga higher truncation point by normalizing the data or by concatenationwith concentration data.

CONCLUSIONS

This article highlights the suitability of the EDXRF combined withchemometric techniques for the characterization of rock by distinguishingsubtle chemical differences between layers of the substratum. The simpli-city of sample preparation, versatility, and relatively fast determinationmake EDXRF an attractive alternative for the analysis of environmentalsamples. The combination of statistics and EDXRF allow the characteriza-tion based on a petrogenesis process. However, special care should be takento avoid the analysis of non-flat areas or presenting evident signs of physicalalteration on samples. This work will be employed to develop a databasefor mapping and regional exploration.

FIGURE 2 Dendrogram of cluster analysis obtained for rock samples according to their mineralcontent.

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