non-destructive analysis using pxrf: methodology and application to archaeological ceramics

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Research ArticleReceived: 12 November 2010 Revised: 8 May 2011 Accepted: 13 June 2011 Published online in Wiley Online Library: 26 July 2011

(wileyonlinelibrary.com) DOI 10.1002/xrs.1360

Non-destructive analysis using PXRF:methodology and application toarchaeological ceramicsNicola Forster,a∗ Peter Grave,a Nancy Vickeryb and Lisa Kealhoferc

Uncritical application of portable X-ray fluorescence (PXRF) to non-destructive analysis of archaeological ceramics has beenreceived with scepticism. In this article, we present a methodological evaluation of the parameters and constraints for PXRFanalysis of archaeological ceramics. We use experimental matrices that simulate characteristics of archaeological artefactsto demonstrate the impact of (1) surface morphology, (2) organic surface coatings and (3) grain size and mineralogy on non-destructive PXRF analysis. We then apply these parameters to PXRF analysis of heterogeneous handmade ceramics from centralTurkey. We conclude that with appropriate methodology, non-destructive PXRF analysis can be demonstrated to provide ahigh level of accurate and precise geochemical discrimination for archaeological ceramics. Copyright c© 2011 John Wiley &Sons, Ltd.

Introduction

Innovations in X-ray fluorescence (XRF) technology in recent yearshave produced a generation of handheld instruments capable ofnon-destructive, high-resolution, multi-element analysis. Subse-quently, there has been a rapid expansion in the application ofportable X-ray fluorescence (PXRF) use in archaeological prove-nance studies.[1 – 6] However, the analytic parameters that governthe use of miniaturised systems to determine the bulk chemicalcomposition of heterogeneous matrices have not yet been ad-dressed in detail. The purpose of this article is twofold. In orderto develop a methodology for non-destructive PXRF analysis ofcoarse ceramics, we empirically evaluate the impact of variablesurface morphology and matrix heterogeneity on analysis. Sec-ondly, we apply this methodology to the analysis of Chalcolithic(ca 5500–3000 BCE) ceramics from Turkey. We demonstrate thata high degree of compositional accuracy and precision is achiev-able with non-destructive PXRF analysis provided an appropriatesample selection and analysis is applied.

Background

Historically, XRF analysis was a laboratory-based technique usingcomplex sample preparation and bulky and expensive cryogenicSi(Li) detectors. The introduction of thermoelectrically cooled Si-PIN diodes in 1991 and developments to various componentssince then have produced instrumentation that is portable, cost-effective and capable of rapid, high-resolution multi-elementanalysis.[7]

PXRF analysis was made feasible by alterations to coolingand excitation systems in conjunction with the integration ofthe power supply and preamplifier with the instrument itself.The introduction of a two-stage cooler eliminated the need fornitrogen purge and increased the operating temperature range,ideal for analysis in the field. In addition, radioisotopes werereplaced with miniature X-ray tubes. As a result, the strict lawsthat regulate movement of hot radioisotope sources were nolonger applicable, facilitating national and international transport

with the instrument. Resolution and sensitivity to detection oflow- and high-energy X-rays were improved by reducing scatter,attenuation and noise effects from a number of sources includingthe beryllium window, preamplifier feedback and collimation. Theintroduction of pn-junction semiconductor detectors using 500-µm-thick fully depleted wafers increased sensitivity to high-energyX-rays and further reduced electronic noise.

The combination of conveniently portable, high-resolution in-strumentation capable of non-destructive, multi-element analysishas underpinned the rapid growth in the application of PXRF toarchaeological studies. This is because portable instrumentationcapable of ‘non-destructive’ elemental characterisation has obvi-ous benefits where destructive sampling is prohibitive; access tomuseum collections and world heritage-listed archaeological sitesis highly restricted for destructive techniques. PXRF, as it preservesthe physical integrity of artefacts, enables access to large data setsand its capability for rapid analysis ensures this can be completedin a practical timeframe.

However, the advantages conferred by non-destructive PXRFanalysis are offset by limitations in the instrumentation itself (i.e.fewer elements and lower sensitivity than destructive methods)and analytic constraints governed by the nature of the samplesurface and matrix. Irregular surfaces and heterogeneous matricespotentially confound non-destructive quantitative analysis withPXRF.[8 – 10] In conventional XRF, these effects are removed by

∗ Correspondence to: Nicola Forster, Department of Archaeology and Palaeoan-thropology, Archaeomaterials Science Hub, University of New England, NSW2351, Australia. E-mail: [email protected]

a Department of Archaeology and Palaeoanthropology, ArchaeomaterialsScience Hub, University of New England, NSW 2351, Australia

b Department of Earth Sciences, University of New England, NSW 2351, Australia

c Department of Anthropology, Environmental Studies Institute, Santa ClaraUniversity, Santa Clara, CA 95050, USA

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homogenising the sample using physically destructive samplepreparation procedures.[11]

Evaluations of the parameters that constrain in situ PXRFanalysis were conducted in the 1990s. These early generationinstruments used radioactive isotopic excitation sources, relativelyinefficient and now obsolete detectors, and relatively large analyticwindows (25 mm diameter).[12 – 14] For current generation PXRF,the analytic window has been reduced to an active detection areaof 7 mm2 (approx. 3 × 4 mm ellipsoid), resulting in commensuratereductions in the analysed area and thus sample volume. Thishas major implications for PXRF performance (e.g. sensitivity toheterogeneities within the matrix and the likelihood of a singleanalysis being representative of the bulk composition of thematerial). However, studies comparable to those undertaken inthe 1990s are absent for these current generation instruments.

For archaeological applications of PXRF, obsidian remainsthe most successfully characterised material for provenancestudies[2,3,5,15] as the glassy matrix has proven to be highlyamenable to quantitative PXRF analysis. As such, large andcomplex data sets may be evaluated in a relatively short time withminimal methodological development. For example, Sheppardet al. analysed a total of 565 obsidian fragments from fourgeological sources.[4]

Studies on more complex matrices, such as ceramics, require adetailed understanding of the effects of grain size and mineralogy,weathering, surface coatings and surface morphology on XRFphysics. The effects of temper and binding agents, porosity,grain size and irregular surface structure may be significant onPXRF analysis. These potential interferences may preclude the useof PXRF for a particular sample or necessitate changes to dataevaluation, such as restricting elements of interest to heavy traceelements. Some recent studies reverted to physically destructivesample preparation methods (samples were sectioned to exposea flat surface or ground into a fine powder and pelletised) tomitigate such effects.[16 – 20] PXRF has a higher background inrelation to peaks of interest than stationery XRF[21] and poorerdetection limits (LDL) than techniques such as NAA and ICP-MS.As such, the use of PXRF when destructive methodology is usedis questionable; if an artefact must lose its physical integrity, it ispreferable to use analytic techniques which produce a higher levelof sensitivity across a greater range of elements or isotopes.

Experimental

Instrumentation

All analyses were conducted using a PXRF (Bruker Tracer III-V)equipped with a rhodium tube and peltier cooled Si-PIN detectorwith a resolution of approximately 170 eV FHWM at the Mn Kα peakat 5.9 keV (at 1000 counts/s) in an area of 7 mm2. The multi-channelanalyser has a 1024 channel configuration. The Bruker Tracer III-Vis a portable spectrometer increasingly utilised in non-destructivearchaeological research for a variety of sample matrices.[3,5,15,22]

Spectra were processed using a suite of software: Bruker X-RayOps for adjusting tube operating voltage and current settings;S1PXRF for count rate and signal acquisition and Spectra 7.1for background stripping, peak deconvolution and calculation ofnet peak areas. This software may be used to identify elementsdetected in spectra collected and then to generate either netpeak area counts for spectra of ‘unknowns’ or where a samplepopulation is better characterised and uniform in chemicalcomposition, quantitative data based on calibrated regression

curves constructed using samples of comparable matrix andknown composition. The same instrument was used for all analysesso as to generate results that are directly comparable. Instrumentperformance is monitored through regular analysis of an in-housestandard and shows long-term stability.

Analytic settings

Analytic settings (X-ray tube voltage, current, filter) are selected toensure optimal detection of elements of interest in a given matrix.In this study, unless specified otherwise, light element analysis (Si,Ti, Ca) has been conducted in triplicate using 15 keV and 20 µA,and without a filter in the X-ray beam path leading to the sample.We use a vacuum pump during light element analysis to ensureoptimal efficiency of the X-ray tube at these lower energy settingsby removing air from the X-ray tube to the beryllium window andthe beryllium window to the detector beam paths. Air attenuationbetween the sample and instrument may further be reduced bythe use of a helium purge to improve detection of Al, Mg andNa. Heavy element analysis (Fe, Th, Rb, Sr, Y, Zr, Nb) has beenperformed in triplicate in air using 40 keV and 13 µA and a 0.006′′

Cu, 0.001′′ Ti, 0.012′′ Al filter. Intensity results are reported as netpeak area values for X-rays detected over a 300 s live count.

Materials and Methods

Spatial distribution of intensity detection

Miniaturised PXRF spectrometers present challenges for repre-sentative analysis of heterogeneous sample matrices, having asmaller detection area than larger, previous generation instru-ments. A high number of analyses are needed simply to equatea comparable analysis area. Adding another layer of complexity,intensity detection varies across the analytic window, effectivelyfurther reducing this area.

To determine the spatial distribution of intensity detection, asteel target (1 mm diameter) was analysed at 1 mm points on agrid covering the vacuum window (approx. 10 × 9 mm ellipsoid),which is centred over the analytic window. Measurements wereconducted in air and vacuum using heavy and light elementanalysis settings, respectively, and a 180 s count.

Attenuation effects related to surface topography

Archaeological ceramics typically possess a degree of surfaceroughness and/or surface coating, both of which can causeattenuation of incident and fluorescent X-rays. In order toassess these effects, we undertook PXRF analysis on a rangeof experimental surfaces.

Surface morphology

Surface topography typically associated with archaeologicalartefacts was simulated by preparing experimental matrices usingfine-grained clay; a smooth surface (reflects intensity yield underideal analysis conditions), concave (maxima 2.5 mm betweenbase and instrument window) and convex surfaces to representcurved samples, a rough surface with irregular contours (approx.0.5–1 mm deep) and a sample etched with parallel groovesapproximately 1 mm deep and wide, spaced 1 mm apart. Theeffect of orientation was assessed by rotating the grooves in linewith the cardinal points (i.e. 180◦, 90◦, 45◦ and 135◦ relative to

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Non-destructive PXRF analysis of ceramics

the excitation source-detector plane). The fluorescent intensityyield for each morphology type was calculated as a percentage toanalysis of a smooth surface.

Organic films

To simulate the effect of organic films on fluorescent yield, organic-based substances were spread uniformly over a flat surface ofvery fine-grained basalt. These substances were mylar sheeting(approx. 20 µm thick), a dry water-based PVA glue (approx. 10 µm),dry blood (<5 µm) and oil (<5 µm). Organic substances wereselected so as to assess the attenuating effect of organic substancestypically associated with archaeological artefacts from excavationor through curation. The fluorescent intensity yield was calculatedas a percentage to analysis of a clean, flat surface.

Effects of grain size and mineralogy

Archaeological artefacts such as ceramics, stoneware, bricks andlithic tools are typically heterogeneous and may represent arange of mineralogy and grain size. To determine the effectof grain size and mineralogy on the precision of PXRF analysisusing miniaturised systems (and thus smaller analysis volume),ten analyses were performed on rock types of differing grain sizeand mineralogy (described in Table 1). The sample was movedafter each analysis and smooth surfaces were analysed to mitigateeffects of surface roughness. The mean (x), standard deviation(SD), coefficient of variation (CV) and number of analyses requiredto achieve 2, 5 and 10% SE were calculated.

Application: a case study using Chalcolithic ceramics fromTurkey

While experimentally we have demonstrated the effects ofpotential interferences of non-destructive PXRF in isolation, theircombined effect on analytic accuracy and precision is unclear. Inorder to understand the extent of this combined attenuation effect,we prepared archaeological ceramic samples in three differentways for PXRF analysis: (1) completely non-destructive, (2) partiallydestructive (i.e. a surface is sectioned to preclude effects of surfaceinterferences) and (3) physically destructive (i.e. the sample isground into a fine powder).

Sixteen excavated fragments of Chalcolithic ceramics fromfour sites (Troy, Cadır Hoyuk, Camlibel and Buyukkaya) inTurkey were analysed. They were identified as Chalcolithic onthe basis of their typology and stratigraphic context. Thesehandmade ceramics reflect the local lithologies of each siteand have a strong local geochemical signature [samples wereanalysed previously using neutron activation analysis (NAA)]; thischaracteristic enables an assessment of the sensitivity of PXRF

when identifying compositional groups. On the basis of the resultsof our methodology evaluation, samples were selected basedon (1) size (sufficiently large to cover the analytic window andmeet infinite depth requirements) and (2) surface topography.Five replicate analysis of each sample and treatment were used toachieve the required precision (±10% SE).

Multivariate analysis

Principal components analysis (PCA) enables archaeologists toidentify discrete compositional groups within a data set. Thisinformation may then be applied to formulate and test hypotheseson trade and exchange routes and socio-economic relations.The use of multidimensional data sets allows for clusters andsubclusters of compositional groups to be distinguished withhigh precision.[23] Although a higher number of elements arepreferred for optimal sensitivity, calcium and titanium havenot been included in the analysis as they are sensitive tosurface interferences and do not accurately reflect the samplecomposition. A recent study demonstrated that the inclusion ofnon-diagnostic elements obfuscates compositional patterns inceramics.[24] As such, PCA was performed using net peak area datafor elements which showed grouping in bivariate plots: Fe, Th, Rb,Sr, Y, Zr and Nb.

PCA was used to explore the results for the PXRF analysis of thearchaeological samples through three-dimensional multivariateprojections of compositional groups to identify and interpretchanges in the precision and accuracy of PXRF analysis with eachsample treatment.

Results and Discussion

Spatial distribution of intensity detection

Variation in intensity detection affects the weighting of elementsas a function of their position relative to the analytic window.Subsequently, results may misrepresent the sample compositionand be biased towards elements situated in the region withenhanced detection. Variation in intensity in air is illustrated inFig. 1. Identical patterning is observed under vacuum conditions.The point of maximum intensity response is slightly offset from thecentre of the window; this asymmetry is attributed to the geometricconfiguration of the incident and fluorescent beam collimators.Intensity response then progressively decreases towards the edgeof the analytic window. This effect, the so-called penumbra affect,is due to collimation of the detector.

As a result, elements situated near the centre of the analyticwindow contribute relatively more to the fluorescence signal thanelements at the periphery. While in a homogeneous material (i.e.

Table 1. Mineralogy of five silicate matrices examined in this study

Grain size Description

Diorite Fine Plutonic rock with intermediate composition. Massive, equigranular, groundmass with phenocrysts ofplagioclase, pyroxene, quartz and hornblende

Dolerite Fine Massive, greyish green rock. Groundmass consists of plagioclase and olivine crystals

Granite Medium Massive, consisting of 3–4% biotite, 30–40% quartz and feldspar (40% orthoclase and 20–30% plagioclase)

Hawaiite Very fine Equigranular, massive, greenish grey rock. Occasional coarse olivine crystal. Gas vesicles cemented by calciteand iron oxides

Ohio red clay Very fine Homogenous clay. Massive

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Figure 1. Thermal map showing variation in intensity detection across theanalytic window. Intensity values (as net peak area) were determined bymeasuring Fe intensity response as a pinhead was analysed at knownpoints. Analyses were conducted in air (using 40 keV, 13 µA, 0.006′′ Cu,0.001′′ Ti, 0.012′′ Al filter). The same pattern is observed under vacuum.

very fine-grained, uniform dispersion of elements) a comparablesample matrix would be analysed each time, in a heterogeneousmatrix the mineralogy in this region may differ significantlybetween analyses. Subsequently, analysis will have low precision.

While a narrow analytic window is beneficial for the analysis ofsmaller artefacts, it is necessary to ensure a sampling strategy isused so the bulk composition of the material is represented.

Attenuation effects related to surface topography

Surface interferences can cause attenuation of incident andfluorescent X-rays. They can be either structural (i.e. surfaceirregularities of sufficient depth to cause air attenuation orsecondary matrix attenuation) or a physical barrier (i.e. a layeron the surface of the sample of sufficient depth and compositionto absorb fluorescent X-rays).

Surface morphology

Irregular surface morphology can play a significant role indistorting analytic results for non-destructive PXRF. Air attenuatesX-rays, causing an exponential drop in intensity as a functionof distance. Further reductions to intensity can occur if surfacestructures obstruct the path of emerging fluorescent X-rays to thedetector. The so-called ‘shielding effect’ then causes secondarymatrix attenuation.[11]

The percent intensity yield relative to ideal conditions (i.e.a smooth surface) is illustrated for different morphology types(Fig. 2). Studies have demonstrated that low-energy X-rays areattenuated differentially by air and consistently have a lowerfluorescent yield, particularly at large distances.[13] This is notobserved empirically with irregular surface morphology. Generally,Si and Ca are affected disproportionately; however, Ti yield isgreater than Zr for concave and rough surfaces. Likewise, eventhough Fe has a lower fluorescent X-ray energy than Rb, Sr, Zrand Nb, it shows comparable intensity yield. We interpret this to

Figure 2. Fluorescent X-ray intensity detection varies with surface morphology. Net peak area values (Table 1) were used to calculate the percent intensityyield relative to that of a flat surface, e.g. I(Si)concave/I(Si)flat × 100. Surface morphologies examined were convex, concave, rough and etched (withgrooves oriented at 180◦ , 90◦ , 45◦ and 135◦ relative to the excitation source-detector plane). Si, Ca and Ti were measured using light element analysisconditions (15 keV, 20 µA, vacuum). Fe, Rb, Sr, Zr and Nb were measured in air using heavy element analysis conditions (40 keV, 13 µA, 0.006′′ Cu, 0.001′′Ti, 0.012′′ Al filter).


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mean that surface experimental noise due to surface irregularitiesis particularly significant for light elements.

Variation in intensity detection across the analytic window incombination with air attenuation effects impact the intensity yieldfor different surface morphology types. Surface convexity does notlower the intensity yield as placement of the sample over the centreof the window ensures the sample is flush against the windowin the area of maximum intensity detection. Increased Si yield isattributed to the curvature of the sample resting slightly in thegroove of the analytic window, reducing effects of air attenuation.The only region with air attenuation is at the window periphery,where there is a negligible contribution to the overall intensitysignal. Conversely, in the case of surface concavity, air attenuationis significant over the area of the window with maximum intensityresponse and the area of the sample that is closer to the instrumentis detected inefficiently. As a result, intensity yield is significantlydecreased for light and heavy elements; Ca yield decreases 52%and Zr decreases 31%. Rough surface topography causes airattenuation of fluorescent X-rays, causing a decrease in yield of3–18%.

Light and heavy elements present distinct grouping in intensityyield response to surface etching. There is clear effect of airattenuation; the fluorescent yield decreases for all etched samples,with fluorescent X-rays of light elements most sensitive to airattenuation and secondary matrix attenuation. The walls of thegrooves effectively ‘shield’ fluorescent X-rays from the detector.For optimal intensity yield, grooves should be oriented so they areparallel to the length of the window.

It is typically the case that archaeological artefacts have somesurface roughness. Irregular morphology can preclude reliablequantitative PXRF analysis and can impact semi-quantitativeanalysis. Correction methods that normalise against Comptonand Rayleigh scatter have been proposed[12,13]; however, this isnot possible given the current constraints of the Bruker softwarepackage. In addition, a flat surface suitable for calculating acorrection factor is frequently absent. In some cases, elementalratios can be used reliably to identify compositional groups. InTable 2, we report the impact of different surface morphologyon the accuracy of elemental ratios between light and heavyelements.

It is evident that surface irregularities have the potential todistort elemental ratios in non-destructive PXRF analysis. For allelements of interest, including higher atomic number elements,concavity prohibits reliable use of elemental ratios. Heavy elementswith an overlapping absorption edge are less sensitive to distortionof ratios[13] and for all other morphology types, the Rb : Zr

elemental ratio is within ±5%. Irregular surface morphologyprecludes light element comparative analysis; the Si : Zr ratio isdistorted in the presence of any irregularity and the Si : Ti ratiois maintained only in etched materials. The ratio of Fe to heavyelements is sensitive to surface irregularity; however, in almost allcases elemental ratios are within ±10%.

From this we conclude that reliable semi-quantitative analysisdepends on (1) selection of samples based on appropriatesurface morphology and (2) appropriate selection of elementsfor comparative analysis. Accuracy within ±10% (and frequentlybetter) is possible for elements with an atomic number ≥26 (Fe)when irregular surface structures are shallow and there is noconcavity.

Organic coating

Organic coatings typically occur on geological and archaeologicalartefacts and have the potential to affect analytic results for PXRF.Such coatings may be due to natural taphonomic processes orbe residual evidence of their past use. Surface coatings attenuateincident and fluorescent X-rays and may also contribute to theintensity signal. The degree of attenuation is affected by theenergy of the fluorescent X-ray and the coating composition. It iswell recognised that surface layers interfere with analysis of thesubstrate; in this article, we demonstrate empirically the impactof coatings typically encountered in the archaeological recordon PXRF analysis. The calculated percentage intensity yields forsamples with organic coatings relative to clean surface analysisare presented in Fig. 3.

Low-energy fluorescent X-rays are generally more affected thanthose with higher fluorescent energy. Si yield is consistentlylower than that of Ca, which is then lower than Fe. It appearsbeyond this point, penetration depth and attenuation has marginaldifference on yield. In conjunction with results demonstrating(generally) differential impact with irregular surface morphology,this indicates that low-energy X-rays are highly sensitive tointerference. Unless the sample surface is flat and clean, it isnot possible to have reliable non-destructive semi-quantitative orquantitative light element analysis.

Given comparable organic composition, surface coating thick-ness is the primary mechanism for fluorescent intensity interfer-ence. Mylar sheeting absorbs approximately 80% of Si fluorescentintensity compared to 50% by glue. Blood and oil smears are verythin and have the smallest effect on yield. Thus, when assessingthe viability of an artefact for PXRF analysis when organic residuesare present on the surface, attention should primarily be paid tothe thickness of the organic layer.

Table 2. Surface morphology has the impact to distort elemental ratios in PXRF analysis

Si : Ti Si : Zr Fe : Zr Rb : Zr

±5% ±10% ±5% ±10% ±5% ±10% ±5% ±10%

Convex × × × √ × √ √ √Concave × × × × × × × ×Rough × × × √ × √ √ √Etched (at 180◦)

√ √ × √ √ √ √ √Etched (at 90◦)

√ √ × × √ √ √ √Etched (at 45◦)

√ √ × × √ √ √ √Etched (at 135◦)

√ √ × × √ √ √ √

The accuracy of elemental ratios (within ±5 and 10%) between light and heavy elements is indicated for convex, concave, rough and etched surfaces.


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Figure 3. Fluorescent X-ray intensity detection of light elements decreaseswith surface coating. Net peak area values were used to calculatethe percent intensity yield relative to that with a clean surface, e.g.I(Si)mylar/I(Si)clean. Surface coatings evaluated were mylar sheeting,water-based glue and blood and oil smears. Si, Ca and Fe were measuredusing light element analysis conditions (15 keV, 20 µA, vacuum). Sr wasmeasured in air using heavy element analysis conditions (40 keV, 13 µA,0.006′′ Cu, 0.001′′ Ti, 0.012′′ Al filter).

Effects of grain size and mineralogy

While the breadth of the area analysed by PXRF is governed bythe active detection area, the depth analysed is a function of thecritical penetration depth for each element. Critical penetration

depth increases with fluorescent X-ray energy and this is matrix-dependent as a function of element composition and density.The depth of critical penetration (x99%) does not adequatelyconvey the effects of matrix attenuation on sample volume. Pottset aladdressed this issue previously and calculated the depths for50, 80, 90 and 99% contribution to intensity signal for a range ofaluminosilicate matrices.[14]

The sample volume analysed using current generation PXRF issmall relative to previous instruments. As a result, heterogeneitiesare proportionally larger and analysis is more sensitive to theeffects of grain size and mineralogy. A single analysis may be apoor indicator of the bulk chemical composition of heterogeneousmatrices. Traditionally, sample preparation has been used tomitigate effects of poor mineral dispersion due to grain size andmineralogy effects. This option is not possible for non-destructiveanalysis.

The volume of excited sample is a function of the beamdiameter and incident X-ray penetration depth. The volumethat then contributes to the detected intensity is reduced. Therelative weighting of detected elements is affected by theirhorizontal distribution (due to spatial variation in detectedintensity magnitude) and their vertical distribution (due toincreased attenuation of the fluorescent signal through the matrix).

As expected, results indicate (Table 3) that coarse-grainedmaterials require more analyses to obtain high precision. For 10%SE of the mean, elements in Ohio red clay require 1–2 analyses,compared to 1–11 for those in diorite and 1–42 for granite.

Table 3. Ten replicate analyses were performed on diorite, dolerite, granite and Hawaiite rocks and Ohio red clay

Si Ca Ti Fe Rb Sr Y Zr Nb

Diorite Mean 150 660 204 810 23 630 25 750 1390 8160 300 3510 100

CV 8 18 17 19 9 13 15 8 32

10% std error 1 4 3 4 1 2 3 1 11

5% std error 3 13 12 14 4 8 9 3 42

2% std error 15 80 72 87 21 46 56 16 258

Dolerite Mean 99 770 192 200 54 900 43 960 230 8960 230 2760 480

CV 2 6 6 4 10 4 15 4 10

10% std error 1 1 1 1 2 1 3 1 1

5% std error 1 2 2 1 5 1 10 1 4

2% std error 1 8 10 4 28 5 58 5 25

Granite Mean 227 860 13 380 3590 3590 6440 670 470 1490 280

CV 8 39 64 28 12 12 26 26 41

10% std error 1 16 42 8 2 2 7 7 17

5% std error 3 62 165 31 6 7 27 27 68

2% std error 16 388 1030 191 34 39 166 165 422

Hawaiite Mean 100 940 143 960 70 160 45 350 160 14 870 270 6750 1340

CV 9 7 11 12 20 4 8 4 6

10% std error 1 1 2 2 4 1 1 1 1

5% std error 4 2 5 6 16 1 3 1 2

2% std error 23 11 28 37 97 5 16 4 10

Ohio red clay Mean 96 500 2390 23 880 29 830 2630 1210 720 5580 460

CV 1 11 1 1 3 4 7 1 9

10% std error 1 2 1 1 1 1 1 1 1

5% std error 1 6 1 1 1 1 3 1 4

2% std error 1 32 1 1 2 4 14 1 20

Light element analysis conditions (12 keV, 20 µA, vacuum) were used to determine Si, Ca and Ti intensity. Heavy element analysis conditions (40 keV,13 µA, 0.006′′ Cu, 0.001′′ Ti, 0.012′′ Al filter in air) were used to determine Fe, Rb, Sr, Y, Zr and Nb intensity. The mean net peak area, coefficient ofvariation and number of analyses required for 2, 5 and 10% SE were calculated.


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(a)

(b)

(c)

(d)

(e)

(f)

Figure 4. PXRF net peak area correlates to quantitative NAA data with a high degree of accuracy. Linear regressions are illustrated for Ca, Fe and Rb forpowdered (a–c) and non-destructive (d–f) sample treatments.

Higher precision (2 or 5%) is obtained by increasing thenumber of analyses. Frequently, the number of replicates thenrequired is prohibitive. A fine-grained, homogeneous matrix suchas Ohio red clay requires 20 analyses for 2% SE of the mean forniobium. Elements that are relatively evenly dispersed betweenminerals require less; strontium requires only four analyses forcomparable precision. Coarse-grain matrices require significantlymore analyses for high precision. Granite requires 7 analyses for 5%SE of the mean for strontium and 68 analyses for niobium. For 2%SE of the mean, this increases to 39 analyses for strontium and 422for niobium. Such precision is unattainable using methodologysuited to PXRF.

Replicate analyses of a range of very fine- to medium-grainedgeological samples represent the possible precision that canbe achieved with in situ PXRF analysis. For very fine- and fine-grained matrices, five replicates are usually sufficient to achieve10% SE. Whereas previous generation instruments could achievecomparable precision with coarse-grained materials, the reductionin sample volume has strong implications on what matrix typescan be analysed and the number of replicates required. Elementsthat exist only in particular minerals are particularly susceptibleto sample volume effects, requiring a much higher numberof analyses for precise PXRF analysis. Thus, while miniaturised

spectrometers offer more convenience, more time may be requiredfor analysis.

PXRF Analysis of Chalcolithic Ceramics fromTurkey

Accuracy of non-destructive PXRF

We performed linear regressions for shared elements betweenNAA and PXRF to evaluate the accuracy of semi-quantitative PXRFanalysis. The correlation between PXRF net peak area and NAAquantitative results is graphed for calcium, iron and rubidiumin Fig. 4(a)–(f) for non-destructive and destructive (powdered)sample preparation.

PXRF analysis of calcium in a powder matrix has a highcorrelation (R2 of 0.95) to NAA; however, there is poor correlationto non-destructive analysis. This substantiates data illustrated inFigs 2 and 3 which indicate that non-destructive analysis is notviable for light elements. R2 values for analysis of Fe and Rbfor non-destructive and destructive techniques are comparable,indicating that PXRF analysis of these elements can be considereda valid and accurate technique. It is noted, however, that the slope


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Table 4. Compositional grouping, net peak area mean and coefficient of variation for Chalcolithic samples ground into a fine powder and analysedusing five replicates

Sample Site Group Ca Fe Th Rb Sr Y Zr Nb

Mean CV Mean CV Mean CV Mean CV Mean CV Mean CV Mean CV Mean CV

AIA1102 Troy 1 160 20 19 380 1 150 19 1730 3 3290 3 310 19 3960 4 280 12

AIA1103 Troy 1 830 3 13 570 1 160 7 1300 2 3830 2 330 9 6010 3 190 18

AIA1105 Troy 1 1100 3 15 290 1 160 15 1460 3 4080 2 370 11 4100 3 190 18

AIA1111 Troy 1 340 14 16 120 1 220 9 1740 3 4010 1 290 17 3380 3 170 22

AIA3471 Cadır 2 1150 10 17 040 2 160 9 1260 3 8570 2 300 12 4500 3 250 13

AIA3472 Cadır 2 1400 7 17 130 2 140 19 1210 5 9430 3 390 11 5110 2 290 9

AIA3473 Cadır 3 850 4 14 574 1 220 18 1740 1 6780 0 340 15 8370 1 380 17

AIA3481 Cadır 3 370 14 16 690 2 240 3 1890 3 6800 2 410 10 10 050 1 440 4

AIA4968 Buyukkaya 4 370 15 28 140 1 130 13 1410 3 2810 3 320 15 3030 2 220 17

AIA4975 Buyukkaya 5 900 4 38 530 1 60 43 410 10 2440 1 250 13 2340 2 180 8

AIA4976 Buyukkaya 4 1120 2 26 910 1 110 14 1070 4 3510 1 360 2 2550 5 210 21

AIA4988 Buyukkaya 4 410 9 26 860 1 130 15 1400 3 2390 2 340 9 2610 3 220 11

AIA4991 Camlibel 6 1480 8 42 050 0 70 16 280 10 2740 1 310 7 2700 2 510 9

AIA4992 Camlibel 7 2760 2 31 150 1 50 21 390 8 6980 1 250 18 2550 4 490 5

AIA4994 Camlibel 5 1530 7 43 940 1 40 48 190 4 1550 2 210 18 1490 5 220 14

AIA4995 Camlibel 8 790 9 38 470 1 110 8 1210 5 2770 3 420 12 4530 2 610 7

Table 5. Compositional grouping, net peak area mean and coefficient of variation for sectioned Chalcolithic samples



AIA1102 Troy 1 190 22 18 850 17 190 24 1680 18 3460 16 360 24 3790 15 320 24

AIA1103 Troy 1 1390 13 17 500 6 170 6 1510 5 4670 10 400 8 5710 14 190 20

AIA1105 Troy 1 1640 11 16 720 3 160 16 1480 7 3980 3 350 7 3820 16 190 18

AIA1111 Troy 1 810 31 21 240 7 210 20 1880 7 4880 12 380 15 4120 26 210 26

AIA3471 Cadır 2 2120 5 22 300 2 200 11 1630 8 10 940 2 410 12 5730 8 350 7

AIA3472 Cadır 2 1880 16 22 300 5 150 20 1360 7 10 960 4 360 5 5340 8 380 8

AIA3473 Cadır 3 1650 11 18 080 2 280 9 2070 10 8450 9 510 13 9540 5 460 9

AIA3481 Cadır 3 760 14 4037 3 300 15 2370 4 8510 3 490 14 12 270 4 570 14

AIA4968 Buyukkaya 4 510 12 34 390 4 170 10 1710 4 3500 8 420 6 3850 5 300 15

AIA4975 Buyukkaya 5 1110 10 43 380 4 90 28 510 5 3100 12 350 8 2880 6 270 15

AIA4976 Buyukkaya 4 1450 7 34 180 2 130 17 1330 5 4420 1 430 5 3310 4 300 7

AIA4988 Buyukkaya 4 520 15 32 310 2 180 7 1760 3 2690 4 470 10 3390 2 280 6

AIA4991 Camlibel 6 1880 5 49 130 5 70 14 320 7 3060 3 350 3 3050 4 540 4

AIA4992 Camlibel 7 3650 8 35 000 2 50 45 450 10 7100 8 260 14 2880 3 530 8

AIA4994 Camlibel 5 2050 10 51 940 3 60 30 260 15 2050 4 240 17 2040 4 310 19

AIA4995 Camlibel 8 950 26 43 270 3 160 8 1410 3 3120 2 520 12 5330 3 730 12

The freshly exposed surface was analysed using five replicates.

of the regression equation differs for each sample treatment,indicating that a linear relationship is maintained only whenthere is a comparable matrix. As such, we conclude that PXRFsemi-quantitative analysis is accurate for elements with an atomicnumber ≥26 (Fe) and is valid for ceramic provenance studieswhere artefacts have a similar matrix.

Precision of non-destructive PXRF

Sample characteristics (variable surface morphology and mineral-ogy) and spectral characteristics (e.g. peak-to-background ratio)affect precision of PXRF analysis. We evaluate the impact of (1) non-destructive, (2) partially destructive (sectioned) and (3) destructive(powdered) sample treatment on the precision of PXRF analysis

and assess the contribution of each variable. The calculated meansand coefficients of variation are reported in Tables 4–6.

Eigenvalues indicate the structure within a dataset and thusthe degree of variation which can be accounted for in the initialcomponents. Eigenvalues for the first four components for analysisof powdered, sectioned and non-destructive samples are reportedin Table 7. In all cases, the degree of variance accounted for in thefirst two components is approximately 75%, indicating each dataset has similar ‘noise’. Evidently, although non-destructive analysishas poor precision relative to powdered samples, this is not theprimary determinant of noise within the dataset.

Powdered samples prepared using destructive methodologydo not present variable surface morphology or mineralogy and as


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Table 6. Compositional grouping, net peak area mean and coefficient of variation for Chalcolithic samples analysed using non-destructivemethodology



AIA1102 Troy 1 1550 63 24 720 9 200 9 1970 7 3930 5 410 10 5130 6 350 6

AIA1103 Troy 1 1720 22 17 510 2 170 9 1590 3 4350 7 350 12 6580 14 220 18

AIA1105 Troy 1 2410 15 17 950 4 160 31 1630 5 4800 4 360 9 4390 12 230 13

AIA1111 Troy 1 1020 11 20 910 3 220 11 1850 10 4710 8 390 13 3130 11 210 22

AIA3471 Cadır 2 1960 7 21 690 2 210 26 1690 8 11 880 4 370 22 5750 2 310 15

AIA3472 Cadır 2 1640 7 20 860 5 170 4 1410 6 11 150 6 390 16 5740 7 390 6

AIA3473 Cadır 3 1390 16 17 960 7 280 12 2060 7 8380 10 480 9 9970 6 470 8

AIA3481 Cadır 3 470 8 20 000 0 310 10 2380 2 8290 3 550 10 12 130 3 540 7

AIA4968 Buyukkaya 4 770 26 34 610 3 190 12 1720 5 3480 5 480 9 3820 1 290 9

AIA4975 Buyukkaya 5 1010 7 41 360 2 70 26 450 5 2440 3 240 13 2580 4 230 12

AIA4976 Buyukkaya 4 2420 42 33 150 5 140 15 1300 4 4380 10 470 15 3300 5 270 7

AIA4988 Buyukkaya 4 950 26 33 730 4 190 11 1810 7 2870 4 450 10 3430 4 310 9

AIA4991 Camlibel 6 2210 11 44 990 3 60 22 310 12 2830 9 330 8 2850 2 530 11

AIA4992 Camlibel 7 2670 45 38 080 10 50 36 460 10 7320 7 260 11 2950 6 550 13

AIA4994 Camlibel 5 1740 11 48 200 8 50 46 210 10 1810 4 210 16 1820 8 260 17

AIA4995 Camlibel 8 840 30 41 630 3 130 12 1350 5 3010 4 510 10 5150 4 680 6

An appropriate surface (no visible deposit, morphology that retains elemental ratios with 10%) was analysed using five replicates.

Table 7. Eigenvalues for principal component analysis of Chalcolithicsamples prepared using destructive (powder), partially destructive(sectioned) and non-destructive methods

Eigenvalue Powder Sectioned Non-destructive

1 55.37 56.04 58.36

2 20.12 20.33 18.68

3 12.46 13.73 13.59

4 6.72 4.18 4.33

% Total variance 94.67 94.28 94.96

such, high variation is attributable to a low peak-to-backgroundratio. This is observed most clearly for Ca, Th, Y and Nb, where ahigh level of variation is observed when the net peak area is lessthan approximately 500 counts.

Sectioned samples, as a freshly exposed and flat surface is usedfor analysis, are not affected by irregular morphology or surfacecoatings. That is, variation can be attributed to low peak-to-background ratio and/or mineralogical effects. Ceramic matricesconsist of base clay which is then tempered with a mineral load.There is relatively high variation in calcium and iron, indicating thepresence of Ca- and Fe-deficient mineral grains dispersed in thematrix. In contrast, high variation in Rb, Sr, Y, Zr and Nb indicatesthe presence of trace element-rich minerals.

Non-destructive analysis is sensitive to the effects of mineralogyand surface interferences, of which elements are impacted differ-entially. Light elements (calcium) have very high variation relativeto powdered and sectioned samples; this is due predominantlyto surface roughness. In the case of sherds AIA1102, AIA2206 andAIA4976, the calcium fluorescent intensity is significantly high indi-cating the presence of a calcite deposit. Variation in trace elementdata is slightly improved compared to sectioned samples. This isattributed to the sample volume analysed; only certain sections of

sample surface were conducive to analysis and it is probable therewas some overlap.

From this we conclude that the combined effects of lowpeak-to-background ratio, surface interference and mineralogyare substantially increased for non-destructive PXRF analysis.Nevertheless, the use of an appropriate number of replicatesensures a mean value is achieved which accurately represents thebulk composition (as is evident in Fig. 4(e)–(f)).

The first three principal components for powdered, sectionedand non-destructive analysis are illustrated in three-dimensionalscatter plots in Fig. 5. While their representation in threedimension differs due to surface and matrix effects, the sameeight compositional groups are identified irrespective of samplepreparation. There is negligible loss in resolution as sampletreatment is reduced; compositional groups remain coherent withnon-destructive analysis. From this we conclude it is possible toachieve accurate and precise non-destructive PXRF analysis ofcoarse ceramics when appropriate methodology is applied.

Compositional groups

Previous analysis has shown that Chalcolithic ceramics were man-ufactured using local sediments.[25] As there is no documentationof trade in handmade earthenware ceramics in Anatolia duringthis period, the compositional groups identified reflect the di-versity in the local geography of each site (Tables 4–6). Thus,the number of compositional groups has reflects the availabilityof local sources suitable for ceramic manufacture and strategiesfor clay procurement. The ceramic sherds from Troy belong to asingle compositional group (1). In comparison, the sherds fromCadır Hoyuk represent two groups (2 and 3), indicating therewere two local sources from which clay sediments were procured.Likewise, we identify multiple local sources that supplied the sitesCamlibel and Buyukkaya (4–8). As expected, these sources overlapon account of the geographical proximity of these sites (approx.3 km).


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N. Forster et al.

(a) (b) (c)

Figure 5. Principal components analysis of 16 Chalcolithic samples from Central Anatolia identifies the same 8 compositional groups irrespective ofsample preparation method: (a) powdered samples; (b) section samples; (c) non-destructive surface analysis.

Conclusion

The major instrumental constraint for current generation PXRFin situ non-destructive analysis is the miniaturisation of PXRF ex-citation and detection technology. This involves a commensuratereduction in the excitation volume, increasing the susceptibilityof PXRF to sample heterogeneity and surface attenuations ef-fects (morphology and coatings). Effective sample volume is alsoaffected by variation in detection response across the analytic win-dow and matrix attenuation of fluorescent X-rays. These factorsreduce the volume analysed and influence the relative weightingof detected elements according to their distribution within thesample matrix.

Sample constraints on PXRF in situ non-destructive analysisinvolve a range of factors, including surface morphology,surface coatings, and grain size and matrix mineralogy. Withoutappropriate corrections these interferences will lead to substantialinaccuracies in analysis and subsequent interpretation. Lightelements are differentially affected by surface interferences,thus optimal sample-to-instrument presentation (i.e. a flat, cleansurface) is essential if they are to be included in the comparativeanalysis. Heavy elements are less sensitive to attenuation bymorphology and organic interferences and elemental ratiosbetween heavy elements are maintained within acceptable limitsin most cases. In the case of very fine- to fine-grained matrices, fivereplicates are adequate to achieve an acceptable measurementerror (<10%).

We apply these parameters to a case study of Chalcolithicceramics to assess their applicability when identifying composi-tional groups using non-destructive methodology. From this weconclude that with appropriate choice of artefacts and method-ology, non-destructive PXRF analysis is capable of discriminatingbetween compositional groups with high sensitivity.

PXRF offers unique advantages to non-destructive analysisof a range of materials for archaeological studies. Currently, itspotential is unrealised as the technology has been applied to onlya limited number of matrices; those that allow for straightforwardanalysis. PXRF non-destructive analysis of archaeological artefactswill continue to be untapped if a strategy for analysis is notsystematically applied to different matrices.

Acknowledgements

This research was supported by the resources of the Archaeomate-rials Science Hub at the University of New England. We thank Bruce

Kaiser for his assistance throughout this study, and Dr Ulf-DeiterSchoop, University of Edinburgh and Dr Sharon Steadman, SUNYCortland, for permission to use artefacts from their excavations.

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non-destructive analysis using pxrf: methodology and application to archaeological ceramics

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