Archaeometric characterisation of ancient pottery belonging to the archaeological site of Novalesa Abbey (Piedmont, Italy) by ICP–MS and spectroscopic techniques coupled to multivariate

Analytica Chimica Acta 537 (2005) 359–375

Archaeometric characterisation of ancient pottery belonging to thearchaeological site of Novalesa Abbey (Piedmont, Italy) by ICP–MS and

spectroscopic techniques coupled to multivariate statistical tools

Emilio Marengoa,∗, Maurizio Acetoa, Elisa Robottia, Maria Cristina Liparotaa,Marco Bobbaa, Gabriella Pantob,1

a Department of Environmental and Life Sciences, University of Eastern Piedmont, Via Bellini 25/G, 15100 Alessandria, Italyb Superintendence of Archaeological Heritage of Piedmont, Piazza San Giovanni 2, 10122 Torino, Italy

Received 26 October 2004; received in revised form 4 January 2005; accepted 4 January 2005Available online 5 March 2005

Abstract

This work presents the archaeometric characterisation of a group of ancient pottery remains discovered during the restoring of the NovalesaA nance andp ly coupledp ) in order too y analysiso©

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bbey (Susa Valley, Turin, Italy) performed in 2000. The characterisation focuses on the achievement of information about proveroduction process of the samples. Firstly, the data concerning the multi-element characterisation of the samples by inductivelasma–mass spectrometry (ICP–MS) were analysed by chemometric tools (principal component analysis and cluster analysisbtain information about their similarity and clustering. These information, integrated with the results of micro-Raman spectroscopf the inclusions shed light on differences in the production process of the samples.2005 Elsevier B.V. All rights reserved.

eywords:Principal component analysis; ICP–MS; Raman spectroscopy; Archaeometry

. Introduction

Archaeometry[1] is a multidisciplinary research branch,hich focuses on studying and solving problems in the fieldf cultural heritage. This discipline is geared towards the ex-

raction of information about the genesis and history of finds,hrough the analysis of the material (which refers to theirhemical structure and modifications) and dating techniques.rchaeometry includes studies about dating, authentication,onservation and restoring, provenance and the achievementf technological information about handmade articles manu-

acture as well. The present paper presents the archaeometricharacterisation of a group of ancient pottery finds, discov-red during the restoration of the Novalesa Abbey (Susa Val-

∗ Corresponding author. Tel.: +39 0131 360272; fax: +39 0131 287416.E-mail addresses:[email protected], [email protected]

E. Marengo), [email protected] (G. Panto).1 Tel.: +39 011 5214069; fax: +39 011 5213145.

ley, Turin, Italy); the characterisation focuses on the achment of information about the samples provenance antechnology used for preparing the potteries.

Some of the most exploited techniques in the studycharacterisation of works-of-art and in particular forstudy of ancient ceramics are atomic absorption spectros(AAS) and emission atomic spectroscopy (AES)[2], X-rayfluorescence (XRF)[2,3], proton induced X-ray emissio(PIXE) [4–7], neutron activation analysis (NAA)[8], induc-tively coupled plasma–mass spectrometry (ICP–MS)[9], X-ray powder diffraction[10,11], reflectance analysis in tregions of visible and NIR[12,13], Raman[14] and IR[15]spectroscopy and photometric analysis[16]. Most of thesetechniques, with the exception of Raman, IR and NIRflectance analysis, are micro-destructive and require a sapre-treatment.

ICP–MS is one of the most important mass spectrommulti-elemental analytical techniques for the characterisaof solid samples in material science[17]. There are sever

003-2670/$ – see front matter © 2005 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2005.01.054

360 E. Marengo et al. / Analytica Chimica Acta 537 (2005) 359–375

applications of ICP–MS in literature for provenance studies,especially for ceramics[9,18–20]. This technique provides aconcentration “fingerprint” of the sample, which can be usedto determine its provenance; besides, it is characterised byan accuracy of the determinations as good as that obtainedwith neutron activation analysis (NAA), commonly used inthe past for provenance study of ceramics[20].

Among the above-mentioned analytical techniques, Ra-man spectroscopy is certainly one of the most suitable meth-ods for the chemical characterisation and for the monitoringof artistic heritage. Micro-Raman spectroscopy, in particu-lar, has already been used in the field of artwork analysis,for example for pigments identification in paintings, frescosand medieval miniatures[21–24]. This technique has beenapplied also for the study of ancient ceramics[25–27], inparticular in order to know the composition of the main con-stituents (often related to their provenance) and of the inclu-sions, i.e. the crystallographic phases (related to the produc-tion process). The technical skill of ancient potters has beenthe subject of much research, since it is one of the most im-portant issues for gaining a deep insight of bygone cultures.

The most important minerals in ceramics, namely quartz,feldspars, carbonates and iron oxides, are well characterisedby their Raman spectra; however, a critical point, is repre-sented by the correlation that can be made between the ce-ramic composition and the mineralogical changes promotedd pro-c dy oft mored allyi f olda sioni

om-p en-t trac-t ari-a fieldo esa om-p f-cap

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cha efer-

ence system characterised by new orthogonal variables calledprincipal components (PCs). Principal component analysisaims to explain the maximum amount of variance availablein the original dataset using as few PCs as possible. The PCscan be used for an effective representation of the system un-der investigation, with a lower number of variables than in theoriginal case. The co-ordinates of the samples in the new ref-erence system are calledscoreswhile the coefficients of thelinear combination describing each PC, i.e. the weights of theoriginal variables on each PC, are calledloadings. The graph-ical representation of the scores allows the identification ofgroups of samples showing similar behaviours (samples closeone to the other in the graph) or different characteristics (sam-ples far from each other). By looking at the correspondingloading plot, it is possible to identify the variables, which areresponsible for the analogies or the differences detected forthe samples in the score plot. From this point of view, PCAis a very powerful visualisation tool, which allows the repre-sentation of multivariate datasets by means of only few PCs,identified as the most relevant.

2.2. Cluster analysis

Cluster analysis techniques[38] allow the investigationof the relationships between the objects or the variables ofa dataset, in order to recognise the existence of groups. IntK s oft sincet

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gicals ly).T log-i tedi hae-o thefi ofp ed byt n ofe oups:e c ma-j mes chae-o anal-y ionsd werec dur-i ingf alu-a have

uring time by several factor such as the productioness, the usage, the burial, etc. In particular, for the stuhe production technique, the firing process has to beeeply investigated, by the characterisation of the therm

nduced chemical transformation, such as destruction ond transformation of new minerals, as well as conver

nto new more stable mineralogical phases[28].The application of chemometric techniques to the c

lex dataset coming from the application of the just mioned instrumental methods, are successful in the exion of systematic and useful information; many multivte statistical tools have already been applied to thef cultural heritage[29–34] for both classification purposnd provenance studies. Many applications of principal conent analysis (PCA)[29–31], soft-independent-model-olass-analogy classification method (SIMCA)[29], clusternalysis (CA)[31] and discriminant analysis[8,32–34], areresent in literature.

In the present paper ICP–MS and micro-Raman specopies were used for the samples characterisation,ultivariate chemometric tools permitted the interpretaf the complex data resulting from the chemical analyse

. Theory

.1. Principal component analysis

PCA [35–37] is a multivariate statistical method, whillows the representation of the original dataset in a new r

his paper, agglomerative hierarchical methods[38] and the-means method[39] are applied: the theoretical aspect

hese techniques are not taken into consideration herehey have already been described elsewhere[38,39].

. Experimental

.1. The samples

The 26 pottery samples were found in the archaeoloite of the Novalesa Abbey (Susa Valley, Piedmont, Itahey were provided by the Superintendence of Archaeo

cal Heritage of Piedmont (Turin, Italy). The samples, lisn Table 1, are named by the label attributed by the arclogists, which refer to the area of the excavation wherends were discovered. InTable 1, the hypothesised typesottery are also reported: these conclusions were deriv

he archaeologists on the basis of a visual examinatioach sample. The samples are thus divided into four grngobed, slip-ware, monochrome engobed, and archai

olica (reported inFig. 1). The samples showing the saample number represent fragments thought by the arlogists to belong to the same find. Such samples weresed separately in order to chemically verify the concluserived by the experts. The samples labelled as “NS”ollected on the surface, while the others were foundng the excavation. All the finds belong to a period rangrom 1200 to 1700–1800, according to a preliminary evtion of the experts that also supposed the samples to

E. Marengo et al. / Analytica Chimica Acta 537 (2005) 359–375 361

Table 1Description of the pottery samples

Pottery sample Type No. of samplings Labels PCA overall dataset Labels PCA reduced dataset

NS 670 Engobed (pot wall) 2 NS1–NS2 NS1NS 671 Engobed (pot wall) 2 NS3–NS4 NS2NE 80 1 IV 41247 Engobed (unguentarium) 3 NE5–NE6–NE7 NE3NS 2404 Engobed (pot wall) 2 NS8–NS9 NS4NE 4297 Slip-ware (lid) 3 NE10–NE11–NE12 NE5NaS 797I 438 Slip-ware (jar rim) 3 NaS13–Nas14–Nas15 NaS6NES 4202 Slip-ware (bowl base) 3 NES16–NES17–NES18 NES7NE 4296 Monochrome engobed (open shaped pot base) 2 NE19–NE20 NE8NS 779 Monochrome engobed (bowl rim) 2 NS21–NS22 NS9NE 80 III 710 Monochrome engobed (open shaped pot base) 2 NE23–NE24 NE10NE 80 1 IV 12 1130 Monochrome engobed (chalice wall) 2 NE25–NE26 NE11NS 20 Monochrome engobed (bowl rim) 2 NS27–NS28 NS12NS 493 Monochrome engobed (bowl wall) 2 NS29–NS30 NS13NE 80 1 III 699 Monochrome engobed (pot handle and rim) 2 NE31–NE32 NE14NE 80 1 III 68/? Monochrome engobed (pot handle and rim) 2 NE33–NE34 NE15NE 80 1 III 697 Monochrome engobed (bowl rim) 2 NE35–NE36 NE16NS 2076 Monochrome engobed (chalice wall) 2 NS37–NS38 NS17NS 72 Monochrome engobed (chalice handle) 2 NS39–NS40 NS18NE 79 1 I 260 Green monochrome engobed (chalice wall) 3 NE41–NE42–NE43 NE19NE 813 4126 Green monochrome engobed (dish rim) 3 NE44–NE45 –NE46 NE20NaS 580 1 X 490 Green monochrome engobed (chalice wall) 3 NaS47–NaS48–NaS49 NaS21NS 780 Monochrome engobed (bowl wall) 2 NS50–NS51 NS22NS 97 Monochrome engobed (bowl wall) 2 NS52–NS53 NS23NE 79 1 I 210 Archaic maiolica (chalice wall) 2 NE54–NE55 NE24NE 80 1 IV 12 1210 Archaic maiolica (chalice wall) 2 NE56–NE57 NE25NaS 79 1 I 153 Archaic maiolica (chalice wall) 1 NaS58 NaS26

Fig. 1. Photos of the pottery samples: (a) engobed unvitrified; (b) slip-ware; (c) monochrome engobed; (d) archaic majolica.


all been produced in Piedmont. The monks at Novalesa didnot produce their own crockery, then they had to buy it fromthe several kilns located near the Abbey: Susa Valley andChisone Valley are among the areas most rich in kilns. Someparticularly fine pottery could come from other regions, likeLombardia or Emilia Romagna, for example, as trousseauaccompanying monks belonging to noble families; this lasthypothesis is however less probable.

ICP–MS and micro-Raman analyses were performed onthe samples in order to obtain information about their elemen-tal content and the presence of specific mineralogical phases;since samples of sure attribution are missing, the performedmeasures could allow to bear out the conclusions derived bythe archaeologists on the basis of only a visual inspection ofthe finds.

3.2. Sampling technique

The sampling method applied consists of the followingthree steps:

(1) the superficial layer is removed, in the points were sam-pling should be performed, by a diamond-coated drill(the superficial layer and the mixture usually show a verydifferent chemical composition);

(2) the fixed sampling portions are then removed by the use

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without interferences from the internal layers. Since the col-lecting was short lasting, all the analyses were performedwith an unfiltered laser beam. The instrumental efficiency incollecting the diffused light is so high that the requested exci-tation laser power is very low: this avoids the thermal damageof the handmade object (non-destructive technique).

The clay matrix of each find was analysed; the measure-ments were performed by focusing the laser beam onto thespots (i.e. the inclusions) showing a different colour (white,red or black) from the whole clay matrix.

3.4. ICP–MS analysis

An X5 ThermoElemental (Winsford, UK) ICP mass spec-trometer was used for elemental analysis. The instrument isequipped with an ASX-500 model CETAC (Omaha, NE,USA) autosampler. Data acquisition and processing wereperformed using the PlasmaLab 2.23 software (ThermoEle-mental). Instrumental and operating conditions are listed inTable 2. With the instrumental configuration described, oxideand doubly charged species formation was found to be <2%.The instrument was tuned daily with a solution containing10�g/l of Li, Y, Ce and Tl.

3.4.1. Sample disgregation and dissolutionTo perform the ICP–MS determination the samples were

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of pliers;3) the removed fragments are reduced to powder in an

mortar (step performed only for ICP–MS analysis).

Different samplings were performed according to thelytical technique to be applied (ICP–MS or micro-Rampectroscopy). For each sample, several fragments weained, in order to evaluate the pottery homogeneity.umber of samplings performed on each sample, for ICPnalysis, is reported in the 3rd column ofTable 1.

.3. Phase analysis by micro-Raman spectroscopy

A spectroscopic analysis by micro-Raman was perforn the samples in order to identify and characterise the iions present in the original mixture; this analysis can pronformation about the technology applied for the producf the potteries.

All analyses were performed by a HR800 micro-Rampectrometer (Jobin Yvon Horiba, Longjumeau, Fra40], equipped with a He–Ne laser emitting at 632 nm wiower of 20 mW. The microscope BX 40 has a focal gradaf 1�m and the objective has a magnifying power of 10×.he instrument is directly controlled by a PC equipped w

he software “LabSpec” (Jobin Yvon Horiba, Longjumerance). The microscope objective focuses the lasersing a back-diffusion configuration and the diffused Ra

ight is collected in the cone defined by the objective itswing to the poor depth of field of the laser beam, only a

hin layer of the sample is illuminated, so the resulting ination mainly comes from the surface of the analysed ob

olubilised by an alkaline fusion, which is suitable for acluminosilicates, as clays, that contain more than 50%iO2: the smelting agent was LiBO2 with a high purity grademong several LiBO2/sample w/w ratios, a ratio of 2 was s

ected as the best one. Operative conditions were optimn the basis of a certified standard clay material (Staneference Material 679, Brick Clay; National Bureau of Sards).

Each collected sample was treated with the same pure, briefly described as follows: 50 mg of powder w

ransferred into a homemade graphite crucible, with 10iBO2; after homogenisation of the mixture, the crucible

ransferred into a muffle at 1100◦C for 30 min. In these conitions, the mixture formed a drop of melted materialas rapidly transferred into a beaker containing 50 m% HNO3 and stirred upon complete dissolution thatchieved after 10 min. The solution was then brought to ame of 125 ml with ultrapure water. In this way, a mother

ution of 400 mg sample/l and 800 mg LiBO2/l was obtainedor each sample, the total dissolved material content bhus 1200 mg/l. To avoid clogging of the ICP–MS interfahe mother solution was further diluted 1:10 with 1% HN3.

volume of solution containing only LiBO2 in the samemount of the samples was prepared; this was utilisereparation of blank and standard solutions, according t

ndications of thematrix matchingmethod.

.4.2. ICP–MS analysisICP–MS operative conditions were optimised on the

is of the reference material NIST SRM 679. The isoto


Table 2Instrumental parameters for ICP–MS analysis

Torch Standard plasma torchNebuliser Concentric typeSpray chamber Impact bead standard quartzSampling cone 1.0 mm diameter nickel coneSkimmer cone 0.7 mm diameter nickel coneForward power 1400 WGas flows

Nebuliser 0.92 l min−1

Auxiliary 1.00 l min−1

Coolant 13.0 l min−1

Torch positionHorizontal 70 stepsVertical 357 stepsSampling depth 36 steps

Voltage lensExtraction −284 VLens 1 +0.8 VLens 2 −41.9 VLens 3 −110 V

Ion drivingFocus 22.5DA −152.6D1 −26.3D2 −123

Mass spectrometerPole bias 1.0Hexapole bias 4.2

Detectionmode Peak jumpingDwell time 10 msSweeps 25Channels 3Acquisition time 60 s

InterferencesCeO+/Ce+ <2%Ba2+/Ba+ <2%

Isotopes used 9Be,23Na,26Mg, 27Al, 29Si,31P,39K, 43Ca,44Ca,45Sc,48Ca, 49Ti, 51V, 52Cr, 54Fe, 55Mn, 57Fe, 59Co, 60Ni,61Ni, 63Cu, 66Zn, 71Ga, 74Ge, 81Br, 85Rb, 88Sr, 89Y,90Zr, 93Nb, 97Mo, 98Mo, 105Pd, 107Ag, 117Sn, 118Sn,120Sn, 121Sb, 127I, 133Cs, 138Ba, 139La, 140Ce, 141Pr,144Nd, 145Nd, 146Nd, 147Sm, 149Sm, 151Eu, 158Gd,159Tb, 160Gd, 163Dy, 164Dy, 165Ho, 166Er, 169Tm,172Yb, 174Yb, 175Lu, 178Hf, 181Ta, 184W, 193Ir, 194Pt,197Au, 205Tl, 208Pb,209Bi, 232Th, 238U

chosen for all elements analysed (Table 2) were selected onthe basis of the response of analyses of the reference material. For each element, the isotopes with the largest relativeabundance were chosen (largest sensitivity); where isobaricor polyatomic interferences were suspected, a less abundanisotope free from interferences was selected or multiple iso-topes of the same element were taken into consideration.

3.4.3. Calibration curveTwo multi-elemental standard solutions were prepared by

reproducing as much as possible a real sample composition:

• Standard solution 1: a 10�g/l solution containing all anal-ysed elements as traces.

• Standard solution 2: a solution containing Na, P, Ca, Ti,and Ba 0.1 mg/l, Mg and K 1 mg/l Al and Si 10 mg/l.

Internal standards were added to all solutions to take intoaccount possible instrumental drifts. Cd, In and Rh, whichare supposed to be missing in the real samples, were chosenfor this purpose and added at 10�g/l concentration. More-over, the analysis of the two standard solutions was replicatedevery 10 samples. Between two subsequent samples, a 30 swashing was settled, followed by an aspiration of the succes-sive sample, preliminary to its measure, in order to eliminatememory effects related to the previous sample analysis.

The final concentrations were calculated as follows:

Conc (ppm)= Conc (ppb)× 1.25

grsmelted sample

When several isotopes were used for each element, the fi-nal concentration was calculated as the average value of theconcentrations obtained for each isotope.

3.5. The dataset

The final dataset consisted of a matrix of size 58× 56(58 being the number of samplings and 56 the number ofconsidered elements), containing the concentrations of eache

per-f f size2 nts)w by at

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lement for each performed sampling.By averaging the concentrations obtained for the

ormed samplings on each sample, a reduced matrix o6× 56 (26 = number of samples; 56 = number of elemeas obtained, after verifying the samples homogeneity

-test.

.6. Software and apparatus

All spectra transformations were performed by ORIG.1 (Microcal Software Inc., USA). ICP–MS analyses werformed by a Thermo Elemental X5 ICP–MS (Therlectron Corporation, USA), equipped by PlasmaLaboftware (Thermo Electron Corporation, USA). Statistalculations, principal component analysis and clustersis were performed by Statistica 6.1 (StatSoft Inc., Und Excel 2000 (Microsoft Corporate, USA).

. Results and discussion

.1. Raman spectroscopy

The inclusions identified in each find are reportedable 3. Some samples proved to be homogeneous by aisual inspection of the clay matrix; nevertheless, analyshe small spots of different colour were performed, but noeresting result was obtained. These samples were confiomogeneous and they are those for which no inclusieported inTable 3.


Table 3Phase analysis of the inclusions of the pottery samples

Sample Quartz Calcite Magnetite Hematite Feldspar

NS 670 XNS 671 XNE 80 1 IV 4 1247 XNS 2404 X XNE 4297 X XNaS 793 I 438 XNES 4202 XNE 4296 XNS 779 XNE 80 III 710 XNE 80 1 IV 12 1130NS 20 XNS 493 X XNE 80 1 III 699 XNE 80 1 III 68/? XNE 80 1 III 697 XNS 2076 XNS 72 XNE 791 I260 XNE 813 4126 XNaS 580 1 X 490NS 780NS 97NE 79 1 I210 XNE 80 1 IV 12 1210 XNaS 79 1 I 153 X

Five different types of inclusions were identified in thepottery samples: quartz, calcite, magnetite, hematite andfeldspar.Quartz. Fig. 2(a) shows the Raman spectrum of an in-

clusion of�-quartz (find NE 8134126). Among SiO2 poly-morphs,�-quartz is the best known and important phase,commonly found in ancient potteries; because of the highenergy required for reconstructive transformations to takeplace, high-temperature forming structures as tridimite andcristobalite are very rarely encountered. The�-quartz Ra-man spectrum shows a prominent peak at 467 cm−1 and amedium intensity peak at 210 cm−1. The presence of quartzgives information about the origin of the potteries; quartzwas used in Piedmont for the covering of engobed pottery,while kaolin was used elsewhere for the same purpose[28].The presence of quartz is then a confirmation that the sam-ples come from Piedmont; on the contrary, it is not possibleto state that finds not showing quartz inclusions do not be-long to the Piedmont area: the performed samplings cannotaccount for the complete absence of quartz inclusions in thewhole pottery sample. The presence of quartz in the largestpart of the samples confirms thus the common origin of thesesamples.Feldspar. Fig. 2(b) shows the Raman spectrum of an in-

clusion of feldspar (find NS 2076). The spectrum is char-acterised by three peaks centred at 455, 475 and 513 cm−1.E ials,t . Thep eries,

does not give relevant information about the techniques usedfor their production, and, as for quartz, its absence in the anal-ysed sample is not an indication of its absence in the wholebody of the pottery. Raman spectra of pure feldspar mineralsare well characterised[41]; however, an exact differentiationamong all silicate forms is very difficult, particularly whenband shape variations and peak shifts occur, due to slightstructural and stoichiometric modifications.Magnetite/hematite. Fig. 2(c) and (d) represent the Raman

spectra of inclusions of magnetite (find NE 791 I 210) andhematite, respectively. Magnetite (Fe3O4) can be identifiedby a broad band around 670 cm−1 which is typical of spinedstructures; hematite (Fe2O3) is instead characterised by a nar-row peak at 300 cm−1, together with bands of medium inten-sity at 230, 412, 500 and 612 cm−1. The presence of mag-netite and hematite provides interesting information aboutthe firing atmosphere. Iron oxides are very influenced by thefiring atmosphere, in fact they differ in colour according tothe firing conditions: when the oxygen is almost absent in thekiln, i.e. under reducing conditions, reduced compounds asmagnetite (black coloured) form; on the contrary, under ox-idising conditions, the oxidised forms as hematite (reddish)prevail. The presence of hematite in some finds could givefurther information about the provenance of the clay used forthe mixture: the Sesia and Chiusella Valleys are in fact veryrich in hematite and the presence, in this area, of kilns wherem medb

in-c isct -e llyf rialdC thes Ca-sds high-t f pri-m daryo oil.C

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ions5 CA,t finalr

P n thed ey arec

ven if feldspar is a common component of clay materhere are only three samples which contain this mineralresence of feldspar, as a common component of pott

onks purchased their pottery in ancient times, is confiry the archaeologists.Calcite. Fig. 2(e) shows the Raman spectrum of an

lusion of calcite (find NE 80 1 III 68/?). This mineralharacterised by a narrow peak at 1086 cm−1 together withwo low intensity peaks at 230 and 130 cm−1. The presnce of calcite (CaCO3) in the finds may occur essentia

or two reasons: a low firing temperature or a post-bueposition process. Calcite exists up to∼800◦C, when theaO formation is promoted, followed by the formation ofo-called “high-temperature crystalline phase” made ofilicates or Ca,Al-silicates such as gehlenite (Ca2Al2SiO7),iopside (CaMgSi2O6) and anorthite (CaAl2Si2O8) [28]. Theimultaneous presence in calcareous clays of calcite andemperature minerals clearly rules out any hypothesis oary calcite, strengthening the assumption of a seconrigin due to deposition induced by water of the burial slearly, in non-calcareous clays calcite is not expected.

.2. Principal component analysis and cluster analysisn the overall dataset

PCA was performed on the data matrix of dimens8× 56. The data were autoscaled before performing P

o eliminate scale effects of different variables on theesult.

The results of PCA are reported inTable 4. The first twoCs account for 45.58% of the total variance contained iataset; these PCs were retained as significant and thonsidered in the following discussion.


Fig. 2. Raman Spectra of the pottery samples inclusions: (a)�-quartz; (b) feldspar; (c) magnetite; (d) hematite; (e) calcite.

The fundamental aim of this first PCA performed on theoverall dataset, is to verify the homogeneity of the samplingsobtained from the same sample.

Fig. 3 represents the loading plot and the score plot ofPC1 and PC2. From the score plot, four groups and a lonelysample can be detected; each of the groups contain all thesamplings belonging to the same pottery sample, thus con-firming the homogeneity of the samples. The lonely sampleis the only one whose sampling was not replicated. From thescore plot, we can then state that the samplings performedon each sample are homogeneous; however, this conclusionwas further confirmed by the dendrogram (Ward method,Euclidean distances) built on the basis of the overall data

matrix. The result is represented inFig. 4. The dendrogramshows two main groups: A and B; group B can be furtherdivided into groups C and D. The samplings of the samepottery sample are contained in the same of the four identi-

Table 4Results of PCA performed on the overall dataset

No. of PC Eigenvalue E.V. % Cumulative eigenvalue C.V. %

1 18.49 33.02 18.49 33.022 7.03 12.56 25.52 45.583 5.21 9.31 30.74 54.894 4.09 7.32 34.83 62.205 2.91 5.19 37.74 67.40


Fig. 3. Loading plot (a) and score plot (b) of PCA of the overall data set. PC1 vs. PC2. Data matrix of dimensions 58× 56 (58 = number of performed samplings;56 = number of elements).

fied groups: so the homogeneity is also confirmed by clusteranalysis.

4.3. Principal component analysis on the reduceddataset

Once the homogeneity was assessed, it was possible tocarry on the statistical analysis on the reduced dataset of size26× 56. The results of PCA performed on the reduced datasetafter autoscaling, gave the results reported inTable 5. The firstfour PCs account for 69.72% of the total variance containedin the dataset and they were retained as significant. The fol-lowing analysis is then performed on the basis of the first fourPCs.

Fig. 5 reports the loading plot and score plot of the firsttwo PCs. For what concerns the first PC, at large nega-tive loadings, lanthanides and actinides are present, whilebivalent cations as calcium, magnesium and strontium andsome transition metals have a large positive weight. PC2 in-

Table 5Results of PCA performed on the reduced dataset

No. of PC Eigenvalue E.V. % Cumulative eigenvalue C.V. %

1 20.84 37.21 20.84 37.212 8.36 14.93 29.20 52.143 5.44 9.72 34.65 61.874 4.40 7.85 39.04 69.725 3.14 5.60 42.18 75.32


Fig. 4. Dendrogram built by the Ward method (Euclidean distances) on the overall data set. Data matrix of dimensions 58× 56 (58 = number of performedsamplings; 56 = number of elements).

stead separates the information concerning bivalent cations(at large positive loadings) from that concerning some tran-sition metals, rare earths and germanium (at large negativeloadings).

The corresponding score plot shows four groups and alonely sample. Groups A1 and A2 are characterised by largeconcentrations of lanthanides and actinides and low con-centrations of the elements presenting large positive load-ings on PC1; however, while group A1 is also characterisedby large concentrations of K and Ba (it lays at positivevalues on PC2), group A2 shows instead large concentra-tions of Sc, Ge, Si (it lays at negative values on PC2). Therare earths are considered geological markers since they donot undergo fractionation during the process of rocks for-mation. This characteristic of geo-chemical uniformity re-flects from clay to the pottery mixture, even if the mate-rial is exposed to high temperatures (i.e. in kilns). Separa-tion of samples on the basis of their rare earths compositioncan then be due to natural differences in the clay composi-tion: these differences can be used for obtaining informationabout the origin of the clays and of the corresponding potterysamples.

Groups B and C show an opposite behaviour with respectto groups A2 and A1, respectively, for what concerns PC1.In particular, group B shows large concentrations of Fe, Ni,Cr: high levels of these elements could be due to the inten-t tia-t us nod thep roupC d; the

presence of large amounts of calcium could be due to severalreasons: (1) the use of calcareous clays in the mixture, i.e.clays of a different origin from the others; (2) the intentionaladding of temperas to the mixture; (3) the presence in themixture of traces and impurities due to carbonates. The pres-ence of calcium can thus have either a natural or an artificialorigin.

Sample NaS26 does not belong to any of the identifiedgroups and it is particularly rich in Sr and Ca and very poorin Si and the lanthanides, characterised by negative loadingson PC2. NaS26 seems different from the other samples, evenon the basis of a simple visual inspection, in facts it appearsconstituted by a white clay matrix.

It is important to stress that samples NS1–NS2,NE14–NE15 and NS22–NS23 are close to each other in thescore plot: this means that they have a similar composition.Each of these couples of samples was supposed by the ar-chaeologists to belong to the same pottery sample: accord-ing to their similar composition, the experts conclusions areconfirmed.

Fig. 6 represents the loading plot and the score plotof PC3 versus PC1. PC3 is characterised by Eu and sometransition elements (Fe, Co, Ni) at positive loadings whileZr and Hf present the largest contribution at negativeloadings.

In the corresponding score plot, several groups can be de-t c-t roupsc r con-t corep ed

ional addition to the clay mixture of temperas. Differenions based upon the content of these elements are thue to natural differences in the clay mixture used forroduction of the pottery samples. For what concerns g, instead, large concentrations of Sr and Ca are recorde

tected: at negative values on PC1, groups A and B are charaerised by large values on the rare earths content; these gontain the same objects already characterised by theient of lanthanides and actinides in the analysis of the slot of PC1 versus PC2. As before, the samples contain


Fig. 5. Loading plot (a) and score plot (b) of PCA of the reduced data set. PC1 vs. PC2. Data matrix of dimensions 26× 56 (26 = number of samples; 56 = numberof elements).

in groups D and E contain large amounts of some transitionelements and very low amounts of rare earths. According tothe information explained by PC3, further considerations canbe pointed out. Group C is rich of Zr and Hf, while it is poorof Eu. Groups A, on the opposite, is characterised by highamounts of rare earths and Eu and low amounts of Zr andHf. Group D differs from group E because of its particularlyhigh concentration of Co, Ni, Mg and partially Eu, while Zrand Hf show low concentrations in the samples belonging tothis group.

As in the previous case, samples belonging to fragmentsthought by the archaeologists to belong to the same find, showsimilar compositions.

Fig. 7 represents the loading plot and score plot of PC1versus PC4. For what regards PC4, Sn and Na present a largenegative loading, while the group constituted by Eu, Ag, Brand Ti shows large positive weights.

According to the corresponding score plot, groups A andB are characterised by large amounts of rare earths, whilegroup C is rich in some transition elements and very poor



of rare earths. Groups A and B can be further characterisedby means of the information accounted for by PC4: group Ashows larger contents of Eu, Ag, Br and smaller contents ofNa and Sn; group B presents an opposite behaviour and it isrich in Na and Sn and poor of the elements showing largepositive loadings on PC4.

Group D is particularly rich of Eu, Ti and Ag, and partic-ularly poor of Na and Sn, and it does not show a particularcomposition with respect to rare earths and transition ele-ments, accounted for by the first PC.

Each time, samples supposed by the experts to belong tothe same pottery are close to each other, thus confirming theirhypothesis.

4.4. Cluster analysis

Two different techniques were applied to verify the pres-ence of groups of samples: (1) a hierarchical method basedon Ward linking and Euclidean distances (applied to thescores of the four significant PCs); (2) a non-hierarchical



method (K-means) applied to the original reduceddataset.

4.4.1. Hierarchical analysisThe Ward method was applied to the scores of the first four

significant PCs. The result is reported inFig. 8. The samplesappear separated in three main groups (A, B and C); group Ccan be further divided in groups D and E. The groups detectedconfirm the hypothesis already driven by means of PCA.

Group A contains the samples showing large positive val-ues on PC1 and PC2 and not particularly large negative valueson PC3 and PC4. These samples are thus characterised by alow concentration of rare earths, a quite large content of sometransitional elements (Ni, Cr, Mn) and, above all, a particu-larly high content of Ca and Sr.

Group B shows large negative values on PC1 and pos-itive ones on the second and the third components. Thesamples belonging to this group are rich in K, Ba, rare


Fig. 8. Dendrogram built by the Ward method (Euclidean distances) on the scores of the samples of the relevant PCs.

earths, Eu, while they are poor in Fe, Ni, Cr, Mn, Zr andHf.

The samples present in group C are all characterised bynegative values on the second PC, thus stressing their lowcontent of Fe, Sc, Ge and Si, and their large content of K,Ba, Ca and Sr. They do not show any particular behaviour forwhat concerns the rare earths content, since they show bothpositive and negative values on PC1. However, these objectscan be further divided in two groups (D and E) on the basis oftheir behaviour along PC3 and PC4: group D shows positivevalues on PC3 and negative values on PC4, while group Epresents an opposite behaviour.

Samples belonging to group D are characterised by largecontents of Eu, Co, Na and Sn and low concentrations ofZr, Hf, Ti; samples belonging to group E show instead theopposite elemental distribution.

Samples belonging to group B are characterised by a largecontent of rare earths which are considered, as just pointedout, to keep their composition even after geological and ar-tificial processes (i.e. exposure to high temperatures or highpressures for long periods); for these reasons, samples ofgroup B are considered as having a common origin.

4.4.2. Non-hierarchical analysisThe K-means method was applied to the reduced au-

t oupsw l in-t aseti esb oups.G showt and 2

Table 6Euclidean distances between the centroids of the groups identified by theK-means method

Group 1 Group 2 Group 3

Group 1 0.00 1.02 1.37Group 2 1.02 0.00 1.09Group 3 1.37 1.09 0.00

appear instead as the most similar (they show the smallestdistance).

Table 7summarises the results: the number of objectspresent in each group and the distance of each object from the

Table 7Results obtained by the application of theK-means method

Group 1, 15elements

Group 2, 6elements

Group 3, 5elements

Sample Distancefromcentroid



NS1 0.81 NE5 0.59 NS9 0.53NS2 0.84 NE10 0.78 NS22 0.51NE3 0.83 NE14 0.98 NS23 0.39NS4 0.97 NE15 0.77 NE24 0.37NaS6 0.93 NE16 0.62 NaS26 0.76NES7 0.58 NE19 0.74NE8 0.70NE11 0.87NS12 0.57NS13 0.69NS17 0.80NS18 0.82NE20 0.73NaS21 0.65NE25 1.06

oscaled dataset; partitions with six, four and three grere applied but the best results according to the fina

erpretation were obtained with a partition of the datnto three groups.Table 6reports the Euclidean distancetween the centroids of each of the three selected grroups 1 and 3 are the most different ones, since they

he largest distance between their centroids. Groups 1


centroid of its own group. The first group is certainly the mostnumerous, while the other two contain six and five objects,respectively. Group 3 shows the smallest distances betweenthe objects and their centroids: it shows the smallest variabil-ity, with the objects being very close to their centroid. Thegroup showing the largest variability is the first one. Groups 2and 3 contain the samples belonging to both groups A and Cobtained by the hierarchical analysis and group 1 correspondsto group B, thus confirming the hypothesis of common ori-gin just made for the samples belonging to group B of thehierarchical analysis.

Fig. 9represents the average plot for the three groups foreach original variable (the average values being autoscaled);for a clearer analysis, the variables were separated in threegroups and three plots are presented. Elements showing alarge average content characterise the corresponding group.

The first plot represents the average composition accord-ing to the elements from beryllium to nickel; group 1 is par-ticularly rich of Be, Al, Si, P, and K, while it is very poorof Mg, Ca and elements from Ti to Ni; Na and Sc show anaverage content close to 0. The second group is instead richof Na, Mg, Si, and elements from Sc to Ni, while it is poorof Be, P, K, Ca. Aluminium shows an average content closeto 0 in this group. Group 3 shows high contents of Mg, Caand Mn and particularly low contents of Be, Na, Al, Si, Sc,Ti and nearly null contents of the other elements.

n ac-c p 1s ele-m rac-t Cs.T h inG ; allt thirdg f allt howp ichs

itiono arests char-a bovea on-t diateb h ofE andU nts,w ighc

roup( sentst ndings orre-s thee ge ors arac-

Fig. 9. Plot of the average of each original variable for the three groups builtbyK-means method (values are autoscaled); elements showing a large aver-age content characterise the corresponding group: (a) average compositionaccording to the elements from beryllium to nickel; (b) average compositionaccording to the elements from copper to barium; (c) average compositionaccording to the elements from lanthanum to uranium.

terise each group. The presence of the bands corresponding tothe maximum and minimum values allows the identificationof the elements showing the smallest variability with respectto the average value calculated.

The three groups are then characterised as follows:

(1) Group 1. This group is rich of Al, Ga, Rb, Nb, Cs, La,Ta, Th, U but it is particularly poor of Cr, Mn, Fe, Co, Niand Au.

The second plot represents the average compositioording to the elements from copper to barium. Grouhows average content above the null value for all theents with the only exception of Sr; this group is cha

erised by particularly high content of Ga, Rb, Y, Nb andhe second group shows a variable behaviour: it is rice, Br and Sb and it is poor in Rb, Sr, Nb, Cs and Ba

he other elements show average values close to 0. Theroup is instead characterised by very low contents o

he elements, with the exception of Sr and Sn (which sarticularly high levels) and Cu, Mo, Pd, Ag and Ba (whhow average values close to 0).

The third plot, which represents the average composf elements from lanthanum to uranium, shows the cleeparation between the three groups. The first group iscterised by the largest content of all the elements (all rare earths) with the exception of Ir and Au (small c

ents) and Eu (close to 0). Group 2 shows an intermeehaviour with respect to the rare earths, but it is very ricu, Ir, Pt and Au. It shows very low contents of Ta, W, Th. The third group is very poor of almost all the elemeith the exception of Ir and Au, which show particularly hontents.

Fig. 10represents the average composition of each gthe average values being autoscaled); each plot reprehe average content of the elements and the correspotandard deviation registered for each element in the cponding group. This analysis allows the selection fromlements just pointed out as presenting a particularly larmall content in each group, i.e. those really able to ch


Fig. 10. Average composition of the three groups for each original variable built by theK-means method (values are autoscaled): (a) average composition andstandard deviation of group 1; (b) average composition and standard deviation of group 2; (c) average composition and standard deviation of group 3.

(2) Group 2. The samples belonging to this group are aboveall rich of Ca and Sr but they show relevant contents ofMg, Mn and Au as well. This group is poor of almostall the other elements: Be, Na, Al, Si, Sc, Zn–Rb, Y–Nbrare earths, Pb, Th, U.

(3) Group 3. The elements showing the highest concentra-tion in this group are Ti, V, Fe, but also Mg, Ni, Eu and Irshow a high content. Very low concentrations are insteadrecorded for Be, K, Rb, Sr, Cs, Ba, Ta, Nb, W, Th and U.

5. Conclusions

Twenty-six pottery samples belonging to the archaeolog-ical site of Novalesa Abbey (Piedmont, Italy) were charac-terised by means of ICP–MS and micro-Raman spectroscopycoupled to chemometric tools, in order to extract informationabout their provenance and their production process.

Micro-Raman spectroscopic analyses provided informa-tion about the production process of the samples. Some sam-


ples proved to be homogeneous and lacking of inclusions(mineralogical phases different with respect to the wholepottery). The most of inclusions are of�-quartz: this con-firms that the samples belong to the Piedmont area, as sug-gested by the archaeologists, since this mineral was used inPiedmont in the covering of the engobed pottery. Samplesshowing magnetite inclusions are characterised by reduc-ing firing conditions, instead the presence of hematite indi-cates oxidising firing conditions. The samples showing inclu-sions of calcite suggest that they were probably fired at lowtemperatures.

For what concerns the elemental composition of each sam-ple, conclusions were derived by means of principal compo-nent analysis and cluster analysis.

A previous PCA performed on the dataset constitutedby multiple samplings of each individual sample confirmedthe homogeneity of each sample. The dataset was thus re-duced, by considering the average concentrations calculatedon the basis of the samplings performed on each potterysample.

PCA performed on the reduced dataset, together withthe application of hierarchical (Ward method, Euclidean dis-tances) and not-hierarchical (K-means) cluster analysis tech-niques, allowed the identification of three main groups ofsamples and an outlier:

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by MIUR (Ministero dell’Universita e della Ricerca, Rome,Italy; COFIN 2003).

References

[1] T.J. Reedy, C.L. Reedy, Archaeometry 36 (1994) 1–23.[2] J.V. Gimeno Adelantado, et al., Spectrochim. Acta Part B 57 (2002)

689–700.[3] F. Cariati, P. Fermo, S. Gilardoni, A. Galli, M. Milazzo, Spectrochim.

Acta Part B 58 (2003) 177–184.[4] A. Denker, M.C. Blaich, Nucl. Instrum. Meth. B 189 (2002)

315–319.[5] A. Denker, J. Opitz-Coutureau, Nucl. Instrum. Meth. B 213 (2004)

677–682.[6] L. Bellot-Gurlet, G. Poupeau, J. Archaeol. Sci. 26 (1999) 855–

860.[7] C.A. Bollong, J. Archaeol. Sci. 24 (1997) 319–327.[8] S. Meloni, M. Oddone, N. Genova, A. Cairo, J. Radioanal. Nucl.

Chem. 3 (2000) 553–558.[9] S. Tunstall, D. Amarasiriwardena, Microchem. J. 73 (2002) 335–347.

[10] E. Alonso, L. Martinez, Build. Environ. 38 (2003) 861–867.[11] K.H. Friolo, B. Stuart, A. Ray, J. Culture Heritage 4 (2003) 211–220.[12] C. Balas, et al., J. Culture Heritage 4 (2003) 330s–337s.[13] M. Attas, et al., J. Culture Heritage 4 (2003) 127–136.[14] D.C. Creagh, V. Otieno-Alego, Nucl. Instrum. Meth. B 213 (2004)

670–676.[15] J.R. Mansfield, M. Attas, C. Majzels, E. Cloutis, C. Collins, H.H.

Mantsch, Vib. Spectrosc. 28 (2002) 59–66.[16] D. Marini, et al., Comp. Net. ISDN Syst. 29 (1997) 1655–1660.[[[[ otal

[ 00)

[ J.

[ age

[ 97)

[[ .[ 0)

[ cal

[ 998)

[[ im.

[ 04)

[ o-

[ 01)

[ , L.88.

[ and90.

[ ters,

A main group characterised by a high content of rare eatypical geological markers. The samples belonging togroup seem to have a common origin, since their comsition of rare earths is quite homogeneous. The archagists confirmed that these samples have a common oor they can be divided into two groups, according to theferent firing technique. PCA and the dendrogram poiout that a further separation of the samples into othergroups is possible.A second group principally characterised by a laamount of calcium. The presence of large amounts ocium could be due to the use of calcareous clays (hypesis of pottery samples of different origin) or to the ustemperas in the clay mixture.The third group is characterised by the large amount ofand transition metals. This peculiar composition couldue to the presence of temperas in the mixture or topresence of residuals of the external layer of the samThe presence of this group does not seem to be relaa different origin of the samples.The only outlier is sample NaS26, which was considpeculiar by the archaeologists as well. This sample cbelong to a different region, perhaps Emilia Romagna

cknowledgements

The authors kindly thank the Superintendence of Culeritage of Piedmont (Turin, Italy), that provided the potamples used in this study. Prof. Emilio Marengo is suppo

17] J.S. Becker, Spectrochim. Acta Part B 57 (2002) 1805–1820.18] J.A. Habicht-Mauche, J. Archaeol. Sci. 29 (2002) 1043–1053.19] H. Neff, J. Archaeol. Sci. 30 (2003) 21–35.20] L.J. Holmes, V.J. Robinson, P.R. Makinson, F.R. Livens, Sci. T

Environ. 173/174 (1995) 345–350.21] A. Perardi, A. Zoppi, E. Castellucci, J. Culture Heritage 1 (20

s269–s272.22] A. Athanassiou, A.E. Hill, T. Fourrier, L. Burgio, R.J.H. Clark,

Culture Heritage 1 (2000) S209–S213.23] S. Bruni, F. Cariati, F. Casadio, V. Guglielmini, J. Culture Herit

4 (2001) 291–296.24] L. Burgio, D.A. Ciomartan, R.J.H. Clark, J. Mol. Struct. 405 (19

1–11.25] P. Colomban, J. Non-Cryst. Sol. 323 (2003) 180–187.26] D.G. Smith, R.J. Clark, J. Archaeol. Sci. 31 (2004) 1137–116027] V.A. Maroni, A.K. Ficher, K.T. Wu, Physica C 341 (200

2243–2244.28] M. Maggetti, in: J.S. Olin, A.D. Franklin (Eds.), Archaeologi

Ceramics, Smithsonian Institution Press, 1982, pp. 121–133.29] G. Musumarra, M. Fichera, Chemometr. Intell. Lab. Syst. 44 (1

362–363.30] M. Attas, et al., J. Culture Heritage 4 (2003) 27–136.31] F. Cariati, P. Fermo, S. Gilardoni, A. Galli, M. Milazzo, Spectroch

Acta Part B 58 (2003) 177–184.32] A. Schwedt, H. Mommsen, J. Archaeol. Sci. 31 (9) (20

1251–1258.33] C.S. Munita, R.P. Paiva, M.A. Alves, P.M.S. de Oliveira, E.F. M

mose, J. Trace Microprobe Tech. 21 (4) (2003) 697–706.34] D. Attanasiou, G. Bultrini, G.M. Ingo, Archaeometry 43 (20

529–547.35] D.L. Massart, B.G.M. Vanderginste, S.N. Deming, Y. Michotte

Kaufman, Chemometrics: A Textbook, Elsevier, Amsterdam, 1936] R.G. Brereton, Chemometrics: Application of Mathematics

Statistics to Laboratory Systems, Ellis Norwood, New York, 1937] G.E.P. Box, W.G. Hunter, J.S. Hunter, Statistics for Experimen

Wiley, New York, 1978.


[38] D.L. Massart, L. Kaufman, Hierarchical clustering methods, in: P.J.Elving, J.D. Winefordner (Eds.), The Interpretation of AnalyticalChemical Data by the Use of Cluster Analysis, Wiley, New York,1983.

[39] J.B. MacQueen, Some methods for classification and analysis of mul-tivariate observations, in: Proceedings of the Fifth Berkeley Sympo-

sium on Mathematical Statistics and Probability, vol. 1, Universityof California Press, Berkeley, 1967, pp. 281–297.

[40] HR 800 MicroRaman Spectrometer, Jobin Yvon Horiba,Longjumeau, France.

[41] T.P. Mernagh, J. Raman Spectrosc. 22 (1991) 453–457.

Documents

Archaeometric characterisation of ancient pottery belonging to the archaeological site of Novalesa Abbey (Piedmont, Italy) by ICP–MS and spectroscopic techniques coupled to multivariate