Research Article  Open Access
Gloria Vaggelli, Margherita Serra, Roberto Cossio, Alessandro Borghi, "A New Approach for Provenance Studies of Archaeological Finds: Inferences from Trace Elements in Carbonate Minerals of Alpine White Marbles by a BenchtoTop μXRF Spectrometer", International Journal of Mineralogy, vol. 2014, Article ID 217916, 11 pages, 2014. https://doi.org/10.1155/2014/217916
A New Approach for Provenance Studies of Archaeological Finds: Inferences from Trace Elements in Carbonate Minerals of Alpine White Marbles by a BenchtoTop μXRF Spectrometer
Abstract
The metamorphic rocks outcropping in the Western Alps are characterised by a great variety of white marbles which have been used since the antiquity. This variety mostly includes nine historical Piedmont white marbles (Ornavasso, Candoglia, Crevola, Pont Canavese, Foresto, Chianocco, Prali, Brossasco, and Garessio marbles) coming from wellknown quarry sites and belonging to different metamorphic geological units of the Western Alps. The petrographical, minerochemical, and C–O isotopic data of these white marbles have been integrated with CaO and trace element (Fe, Mn, and Sr) concentration determined on single crystals of carbonate minerals (i.e., calcite and/or dolomite) by means of a benchtotop µXRF spectrometer. Principal component analysis and hierarchical cluster analysis were performed on a data set of 178 observations containing CaO, Fe, Mn, and Sr concentration as well as the maximum grain size (MGS), δ^{18}O and δ^{13}C. The use of only five selected variables (CaO, Fe, Mn, Sr, and δ^{18}O) has provided the correct allocation of each individual observation to its relevant class. Therefore, this approach based mostly on a noninvasive µXRF determination will be useful to define the provenance of unknown marbles of alpine origin used in antiquity for cultural heritage.
Dedicated to the memory of Margherita Serra, unforgettable Ph.D. Student and friend who early left an indelible sign behind her
1. Introduction
One of the most difficult problems that archaeometry has faced in recent decades is certainly the identification of white marbles used in antiquity. Determining the provenance of marbles used in antiquity for architectural and sculptural artefacts can, indeed, yield important archaeological information on their history. Up to nowadays, different methods have been developed to distinguish white marbles coming from different quarry districts [1]. The optimal first steps in sourcing a lithic artefact should be the macroscopic and thin section study with the petrographic microscope. Unfortunately, it is often the case that it is not allowed to take a sample which is suitable for thin section preparation from precious artefacts. Instead, a common procedure is to analyse powder samples in various ways. The system most widely used today is that of determining C–O isotopic signatures [2–5]. However, with the rapidly growing of databank, the compositional fields in the isotope discriminative diagrams have been progressively expanded, so many classical marbles show relatively large overlapping ranges.
Other techniques employed for marble characterization are cathodoluminescence [6, 7], trace and rare earth element contents [8–10], electron paramagnetic/spin resonance [11], and ^{87}Sr/^{86}Sr isotopic ratios [12–14].
Chemical analyses were also attempted as a criterion for ascribing a marble to a specific source, and a large number of papers (see e.g., [15]) on this topic have been published following the first work by [16]. Multielement neutron activation analysis of various trace elements was applied to pinpoint the origins of marbles, and this method has been used successfully for characterizing marble quarries in different geographic areas [8, 17–19].
However, single provenance determinations using only one method are still insufficient because overlapping results within and between different sample regions occur. Therefore, a number of techniques have been combined to precisely determine the marble provenance [6, 10, 21–27].
Aim of this paper is to present a new chemical approach to discriminate white marbles, in addition to the conventionally used methods based on petrography, cathodoluminescence, and stable isotope analysis, in order to ascribe their provenance to a specific quarry site or at least to a defined geological unit. Abandoning the idea of a traditional approach to bulk analysis, this paper aims to evaluate the effectiveness of a new methodological approach based on major and trace element characterization of carbonate mineral phases, calcite and dolomite, being the main rockforming minerals of white marbles.
As μXRF technique is a prominent system for the nondestructive and/or noninvasive analysis of trace elements in glasses [28] and in minerals occurring in ornamental stones from cultural heritage [29, 30], our attention has been focused on the provenance study of marbles used in antiquity by trace elements determination in the main carbonate mineral phase. Therefore, the multianalytical approach based on petrographic (optical and scanning electron microscope), electron microprobe, and stable isotope analysis on calcite and dolomite from white marbles occurring in Western Alps (Piedmont, Italy) reported by [20] has been integrated with trace elements analysis through μXRF. This new analytical protocol was used to characterize and identify different marble varieties and understand the effective discriminative power of trace element patterns in carbonate mineral phases.
2. Geological Setting of Alpine White Marbles
The Western Alps of Northern Italy represent a segment of the AlpineHimalayan orogenic chain associated with the closure of the Western Tethys ocean [31]. Morphologically they are the expression of the continental collision between the European and the Adriatic (African) plates. Many geological units of the Piedmont Western Alps (Northern Italy) have provided a great variety of white marbles (Figure 1) which can be grouped in two classes: the marbles belonging to the PreTriassic (Palaeozoic) crystalline basement of the Western Alps, characterised by polymetamorphic evolution and the Triassic marbles coming from the metasedimentary cover, with monometamorphic evolution. The Palaeozoic marbles include the Ornavasso marble (from IvreaVerbano zone in Ossola valley), the Prali and Brossasco marbles (from Dora Maira Massif), and the Pont Canavese marble (from Sesia Lanzo zone). The Triassic ones consist of Foresto and Chianocco marbles (from the metasedimentary cover of Dora Maira Massif), Crevola marble (from the deepest Penninic unit in Ossola valley), and finally the Garessio marble (from Briançonnais zone).
Less known than the more famous Apuan varieties (the wellknown Carrara marbles), white marbles from western Alps (Piedmont region) have been widely used in the past for sculptural and architectural purposes [32]. Since Roman times to late Baroque, Piedmont region shows the signs of an intensive quarry activity, which results in renowned examples from the Augustus Arc in Susa to the Savoy architecture in Turin.
Besides the interest for provenance purposes, Piedmont Alpine white marbles provide also the opportunity to investigate variations in trace elements composition according to different pressuretemperature conditions and to combine the microchemical results with C–O isotope data on the same marble. The main varieties, indeed, occur as small lenses intercalated to schists and gneisses belonging to separate geological units which suffered different metamorphic conditions (i.e., different  genetic conditions) during different times.
3. Samples and Experimental Techniques
3.1. Sample Descriptions
This work has been carried out on white marbles collected in 9 different quarries located in Piedmont Western Alps. Approximate geographical locations of the quarries are given in Figure 1.
The studied marbles come from the private collection of DSTUNITO and were previously studied by [20]. A list of the selected samples corresponding to alpine marble types, commercially known, is reported on Table 1 together with their main petrographic features, C–O isotope data, and metamorphic conditions.
 
AGS and MGS terminology according to [4, 39, 40]. C and O data are expressed versus PDB standard [41]. Analytical protocol according to [42]. Standard deviation: 0.1 (δ%_{0}) for O and for C. *Data from [43]. In acronyms: C: calcite; D: dolomite. 
The studied alpine marbles were classified according to the main carbonate component, calcite (CaCO_{3}) or dolomite [CaMg(CO_{3})_{2}]. The calcitic marbles, are characterized by a maximum 20% dolomite to calcite ratio and correspond to Brossasco, Garessio, Ornavasso, and Candoglia marbles. Marble from Prali, in Dora Maira massif, is mainly calcitic, with a dolomite content up to 50%, and therefore both carbonate phases are almost equally abundant. Finally, over 50% dolomite concentration characterizes Pont Canavese, Foresto, and Chianocco marbles as well as Crevola marble which presents two varieties of commercial interest, showing different mineropetrographic features, though they are exploited in the same quarry site [20].
3.2. Instrumentation
MicroXRF Eagle IIIXPL (Röntgenanalytik Messtechnik GmbH, Germany) was used for trace elements analysis of calcite and dolomite in rock samples.
The system includes a Rh Xray tube working at a maximum voltage of 50 kV and a maximum current of 1 mA. The Xray fluorescence is detected by means of a liquid nitrogen cooled ultrapure Si detector which has an active area of 30 mm^{2} and 5 μm beryllium window. The energy resolution results to be lower than 135 eV. Polycapillary lenses collimate Xray microbeam at the sample surface (30 μm). The sample positioning is controlled by a two CCD video cameras, respectively, with a 10x and 100x magnification and optical focusing. The XYZ stage minimum step is 1.5 μm. The instrument can work both in air or in vacuum and data can be acquired selecting single spot, linescan, or element mapping scan mode.
Peak to background ratio can be optimized in the energetic range of interest with a set of different primary filters and accordingly adjusting analytical conditions. Available primary filters correspond to Al25 μm, Al250 μm, Ti25 μm, Ni25 μm, Nb35 μm, and Rh50 μm thickness. Filtering the primary radiation is one of the most effective methods to reduce the importance of diffraction peaks in the spectra as well as to drastically reduce the counts of lighter elements and to increase the peak/background ratio for heavier elements. It is, therefore, clear that the selection of proper primary filters must be a compromise between the optimization of peak to background ratio in the energetic range of interest and the minimization of coherent scattering phenomena. The Ti25 filter was found proper for the quantification of trace elements in the energetic range of transition metals up to Nb to minimize the intensity of diffraction lines and accordingly avoid drastic reduction of total count rates with heavier filters.
In addition to Tifiltering, the final spectra were obtained combining five spectra acquired on the same spot areas after rotation along a vertical axis located in the centre of the analysed area.
The object of rotating the sample in its analysis plane was to identify and remove artifact lines from the characteristic spectra. A specific facility called Del DiffPeaks in Spc Preview routine of Vision32 software was used for this purpose and the final Xray spectra result from 5 rotations after removal of peaks whose position change with the angle between the incoming radiation and the sample crystalline lattice. Sample rotation angles were defined as separated by 72° because 5fold symmetry (360/5) does not exist in Earth minerals: this means that the 0°, 72°, 140°, 212°, and 288° rotations were chosen. The subtraction of the diffraction contribution, channel by channel, occurs when the component spectra (rotated by 72°) differ by more than 5 times σ: in this case the final spectrum takes the minimum value (i.e., the minimum diffraction contribution) or the average value (i.e., in absence of diffraction).
3.3. Analytical Conditions
Analyses were performed on big samples cut into 4 × 4 × 2 cm flat blocks in order to test the chemical composition homogeneity of calcite. However, the minimum size of the sample, which is connected to the lateral and depth resolution of the method, can be approximately 100 × 100 × 300 μm. In regard to the maximum size, an object of approximately 150 × 150 × 70 mm can be logged entirely inside the sample chamber. Finally, because the lateral resolution of the Xray beam is ≈30 μm, the flatness of the surface affects the result very little but the surface (i.e., the spot area to be analysed) has to be clean; indeed, dirt or patinas could contain the same elements as the sample and possibly invalidate the final quantification.
In addition to C and O which form the carbonate ion, undetectable by microXRF, Ca is the major element for calcite and Ca and Mg for dolomite, respectively. The analytical method proposed for the measurements of trace elements in carbonate phase does not allow measuring Mg. Indeed the use of a Tifilter drastically reduces the Xray counts and in particular light elements with 10 < Z < 16 become more or less undetectable. However, the MgO/CaO ratio is mostly constant in dolomite, and therefore the only quantification of CaO is able to easily distinguish between dolomite and calcite crystals (some 31 and 56 wt.%, resp.). In regard to trace elements, only Fe, Mn, and Sr were the detected elements, which, moreover, correspond to the most abundant and common trace elements occurring in carbonate minerals.
The analytical conditions used for determining CaO and trace elements (Mn, Fe, and Sr) in calcite and dolomite include the following instrumental parameters: voltage, 40 kV; beam current, 1000 μA live time, 1000 s. In addition, a titanium primary filter (25 μm in thickness) was inserted between anode and samples. All analyses were carried out in vacuum conditions even if the vacuum is indispensable only for the detection of light elements (Z < 15). The final composition was quantified adopting the intensity method proposed by [33] using an internal working standard of either calcite (M43) or dolomite (Dolo22), as a result of the identical matrices with respect to calcite and dolomite in the studied samples.
The used calcite working standard is from M43 Carrara marble, internationally used as standard for C and O isotope. It was, first, measured by ICPOES and ICPMS techniques, according to HCl 5% bulk rock dissolution method (CaO = 56 wt.%, Sr = 157 ppm, Fe = 64 ppm, and Mn = 10 ppm) and finally by multiple μXRF tests to check the homogeneity of the calcite grains. M43 calcite was then used to quantify the dolomite working standard (Dolo22 from the standards library of the SPI Supplies and the C. M. Taylor Company) using Fpwith standard method according to [28].
4. Data Treatment
All selected samples (i.e., alpine marble types) are of known provenance; therefore, in this work, we are trying to verify the reliability of the proposed method to discriminate between white marbles using chemical information corroborated by a geomineralogical approach. In the studied marbles, the MGS (and subordinately AGS) as well as the δ^{18}O and δ^{13}C values resulted in the more variable petrographic parameters, and therefore useful for discriminative purposes according to a multivariate analysis approach in addition to the chemical major and trace composition of the main carbonate phase (calcite or dolomite). The recourse to a multivariate statistical treatment of the available data to obtain a classification of samples in groups of common provenance has long been known in the field of archaeometry [8, 34–36].
The parameter determined on the analyzed carbonate crystals (CaO, Fe, Mn, and Sr concentration, MGS, δ^{18}O and δ^{13}C) was used to perform an unsupervised pattern recognition and to identify groups of marbles sharing a possible common provenance. This process was carried out running principal component analyses (PCA) and hierarchical agglomerative cluster analysis (HCA) by means of OriginPro 8.6.0 package (2011OriginLab Corporation: http://www. OriginLab.com/). As the variables are measured on different scales (CaO wt% range, Mn, Fe, Sr ppm range, δ^{18}O, and δ^{13}C δ%_{o} range), the correlation matrix S (covariance matrix weighted by the standard deviation of variable) was used to process the data and to calculate the Eigenvalues and eigenvectors parameters. In this way, scores for each principal component are scaled by the square root of its eigenvalue.
HCA was performed using the squared “Euclidean distance” (geometric distance in multidimensional space) function, where the greater emphasis to objects that are further apart is stressed. The “group average” option was also used, that is, the distance between two clusters calculated as the average distance between all pairs of objects in different clusters.
5. Results and Discussions
5.1. Trace Elements in Calcite and Dolomite
According to the main carbonate phase of each alpine white marble, calcite or dolomite phases were analysed for major and trace element determination. In particular, calcite was analysed in Ornavasso (ORNC) and Candoglia (CANC) as well as in Brossasco (BROC) and Garessio (GARC) marbles, dolomite in Pont Canavese (PONTD), Foresto (FORD), and Chianocco (CHID) marbles and in both Crevola varieties (CRED1; CRED2) while calcite and dolomite, occurring in almost the same amounts, were analysed in Prali marble (PRAC and PRAD, resp.).
Our set of measurements included, on the whole, 178 μXRF spectra, diffraction subtracted, measured on nine different alpine white marbles, using the method described above: Ca was expressed as wt% oxide (CaO), Mn, Fe, and Sr as ppm element (part per million by weight). It has to be pointed out that Crevola marble is represented by two classes corresponding to two varieties (CRED1 and CRED2) as well as Prali marble, which contains similar amounts of calcite and dolomite which were both analyzed (PRAC and PRAD); therefore, the nine marble types are represented by eleven classes.
In Table 2 mean and standard deviation values are reported for each μXRF measured element; these quantities are calculated, for any class, from the number of observations reported in “# points” column: this number ranges from a minimum of 10 in dolomite of Prali (PRAD) to a maximum of 25 in calcite of Foresto marble (FORD) and corresponds to the number of analyzed carbonate crystals. The CaO values, since the standard deviation is low, are very close to theoretical values for calcite (some 56.00 wt %) and dolomite (some 31.00 wt %), respectively.
 
Total # points = 178. C: calcite; D: dolomite. 
Figure 2 shows two scatter plots using Fe (ppm) as common abscissa versus Sr (Figure 2(a)) and Mn (Figure 2(b)), respectively. Sr concentration is below 200 ppm, except for PRAC and PONTD where it increases up to a mean value of and ppm values, respectively. Mn concentration is generally very low (below 70 ppm), except for CANC and ORNC samples where it reaches a mean value of and ppm, respectively. In regard to Fe, it seems the most variable trace element ranging from about 50 ppm in PONTD and CHID marbles to almost 500 ppm in BROC marble, respectively.
(a)
(b)
All the marbles analyzed show compositional zoning rather limited compared to the three trace elements measured. This means that the dispersion of trace elements within the crystal lattice of carbonate minerals is homogeneous and that the method proposed in this work can be worthwhile for discriminating between marbles of different origin.
In Prali marble, the analysis of coexisting and cogenetic calcite and dolomite shows that higher contents of Sr and Fe occur in CaCO_{3} mineral phase, suggesting the higher distribution coefficient of Sr and Fe in calcite than in dolomite as also supported by previous authors studying the temperature dependence of the distribution coefficient of strontium in calcite and dolomite [37, 38].
However, from the observation of diagrams in Figure 2, the random scattering of the points representative of calcite (empty symbols) and dolomite (black symbols) leads to the conclusion that the concentration of trace elements, in each variety of marble, does not depend primarily on the type of carbonate mineral phase.
Despite some experimental field values of marble varieties plot in well distinguishable fields (e.g. PONTD, PRAC, CRED2, CANC, and BROC), in other cases the data points span similar regions in the variable space, without being clearly grouped. Therefore, the discrimination based on scatterplot diagrams appears to be problematic.
For this reason, in order to obtain more quantitative information, discriminant analyses of the data, using the complete sets of variables, were performed. In this way percentages of correct reassignment may be obtained and the discriminating capability of the proposed technique may be assessed.
5.2. Multivariate Analysis
The whole set of our experimental results can be summarized by a data matrix, which contains the values of 8 variables (MGS, AGS, δ^{18}O, δ^{13}C, CaO, Fe, Mn, and Sr) for the 178 data points, categorized into 11 clusters (ORNC, CANC, BROC, GARC, PONTD, FORD, CHID, CRED1, CRED2, PRAC and PRAD).
As the first step, all observations and all eight continuous variables of Tables 1 and 2 were used in the PCA. We obtained a correlation matrix (Table 3) where MGS and AGS are greatly related to each other (0.99), both strongly inversely correlated with δ^{18}O (−0.86 and −0.89, resp.) and directly correlated with Mn (0.80 and 0.74, resp.). δ^{13}C is also moderately correlated with δ^{18}O (0.58).

Therefore, a lower number of variables might be enough discriminative for our purpose. So an input matrix with a number of variables ranging from 3 to 5 (Fe, Mn, Sr, δ^{18}O, CaO, and/or δ^{13}C) was used: all the PCA diagrams obtained are reported in Figure 3. Tables 4 and 5 are reported all statistical data processing connected to PCA calculation (corresponding to Figures 3(b) and 3(d), resp.), where the correlation matrix with its eigenvalues (in descending order) and extracted eigenvectors (PC1 and PC2 coefficients referring to the first 2 eigenvalues) is reported.
(a)  
 
(b)  
 
(c)  

(a)  
 
(b)  
 
(c)  

(a)
(b)
(c)
(d)
About correlation, matrix it is interesting to note the highest inverse correlation (−0.665) between δ^{18}O and Mn in Table 5 and medium correlation of Sr versus Fe (−0.418), CaO versus Mn (0.384), and CaO versus Fe (0.335) in Tables 4 and 5. It is also notable that, by using only the first two principal components (PC1 and PC2), in both cases of Tables 4 and 5, we extract a cumulative percentage of about 70%. Element vectors of Figure 3 refer to PC1 and PC2 coefficient of Tables 4 and 5 (right and top scale) while single scatter points refer to PC1 and PC2 score (bottom and left scale) corresponding to the linear combination of original variable and principal component coefficients.
In Figure 3(a), the scoreplots obtained using trace elements (Mn, Fe, and Sr) show that only the marble type which is also discriminated in the scatter plots of Figure 2 (ORNC, CANC, CRED2, BROC, and PONTD) can be easily distinguished, while partial overlaps remain for the other marble varieties. In Figure 3(b), obtained using trace elements and CaO concentration, a good separation between most marble types, especially between calcite and dolomite marbles, occurs. Only PRAD and CHID marbles are still overlapping. Figure 3(c), where δ^{18}O, δ^{13}C, Mn, Fe, and Sr variables are used, shows that CANC and ORNC partially overlap while a severe overlap occurs between CRED2 and FORD and between CHID and PRAD, respectively. Finally, in Figure 3(d), where δ^{18}O, CaO, Mn, Fe, Sr variables are used, the dolomite marbles are well separated from the calcite types. Moreover, the different varieties are well distinguished except GARC marble which partially overlaps FORD field. In Figures 3(b) and 3(d) there is a better separation, but not fully reached, between the 11 classes of the studied marble. For this reason HCA analysis was performed, using the variable selections of Figures 3(b) and 3(d).
Using the HCA method, the dendrograms of Figures 4 and 5 are obtained: the abscissa corresponds to the progressive observation number (from 1 to 178) and the ordinate reports the normalized square Euclidean distance between observations where the horizontal lines indicate the mergence of two clusters. Figure 4 was performed using 4 variables (CaO, Mn, Fe, and Sr) and Figure 5 using 5 variables (δ^{18}O, CaO, Mn, Fe, and Sr). In both figures, the main discriminative parameter resulted in the CaO contents, which allowed separating the marble variety according to their main forming mineral (dolomite at the left branch and calcite at the right branch). In Figure 4, ten cluster are clearly separate, while in, Figure 5, all eleven marble classes are discriminated.
Looking in more details of the calcitic branch (right side of Figure 4), CANC and ORNC are the first to merge (distance of 0.088) being marbles coming from the same geological unit but outcropping in a different quarry district. PRAC and GARC merge at a larger distance (0.137). Finally, Brossasco marble, which has high AGS and MGS, comparable to Ornavasso/Candoglia marble, results, however, chemically more similar to Prali and Garessio (distance = 0.546). All calcitic marbles become a single cluster at distance of 0.713.
In regard to the dolomitic branch (left side of Figure 4), PRAD and CHID constitute a single cluster as they merge at the minimum distance (0.013). CRED1 and FORD combine at 0.055, and CRED2 merges with the previous cited marbles only at 0.222. Therefore, the two varieties of Crevola marble are significantly far and dissimilar. Finally, PONTD marble differs from all the others and merge, forming a single dolomitic marble cluster, at 0.806. This behavior of Pont Canavese marble is mostly due to the significantly higher Sr concentration as well shown also in scatter plot of Figure 2(a) and PCA plots of Figure 3.
If δ^{18}O variable is added (Figure 5), PRAD and CHID merge at a larger distance (0.062) with respect to Figure 4 (), resulting easier to discriminate. Therefore, HCA with 5 variables allowed separating all 11 clusters. Moreover, in both dolomitic and calcitic branch, a generally significant increase in the distance due to the wide difference in δ^{18}O values is shown (Table 1). However, no changes in clusters occur and the order of hierarchy remains the same. In Table 6, the final centres for each cluster after HCA analysis using four (A) and five (B) variables are reported. Proper grouping observations are obtained for 10 and 11 clusters, respectively (Figures 4 and 5). All variable columns are normalized in the range 01. The distance between the cluster centers always improves passing from 4 to 5 variables: the greatest increase (difference = 0.735) is between CANC and CRED2.
 
All variable columns are normalized in the range 01. # = number of investigated carbonate grains. 
HCA method realizes, in the case of 4 variables, 86% of correct clustering observations since cluster number 4 of Table 6(A) (PRAD/CHID), containing 25 observations (corresponding to 14% of the total), can be considered not properly assigned (Figure 4 and Table 6(A)). In the case of 5 variables, all observations can be considered correctly assigned (Figure 5 and Table 6(B)).
Even if the distance between PRAD and CHID cluster centers remains low (0.062 in Table 6(B)), it, however, allows distinguishing PRAD and CHID and the separation between the different marble types appears complete.
6. Conclusions
In this paper, the petrographical, minerochemical, and C–O isotopic data of white marbles from Western Alps, previously described by means of classical techniques as optical and electron microscope, electron microprobe, and stable isotope analysis [20], have been integrated with trace elements analysis on the carbonate mineral phases (calcite and dolomite) performed by means of a μXRF instrument. The results presented above for the multimethod discrimination of the white marbles from Western Alps are certainly encouraging.
The method here proposed seems capable of allowing discrimination among the Western Alps white marbles with acceptable confidence. In particular, the PCA method, using two principal components, based on a maximum of 5 variables, extracts about 70% of the available variance. HCA method properly assigns 86% of observations to ten clusters using CaO, Mn, Fe, and Sr variables and the total observations to proper eleven clusters using δ^{18}O, CaO, Mn, Fe, and Sr variables.
The possibility of making μXRF trace element analyses on white marbles provided the opportunity of improving previous archaeometric approaches and will allow performing quantitative analysis either on archaeological finds smaller than ≈150 × 150 × 70 mm or on small fragments (at least 100 × 100 × 300 μm) by nondestructive analytical procedures. Obviously, the data presented are still few and should be implemented with other measures to make the proposed procedure more significant. Therefore, the proposed discriminative approach, mostly based on trace elements in the carbonate phase and on the δ^{18}O isotopic signature, will be applied to white marbles from the Mediterranean area in order to obtain a complete characterisation of such important natural stones, used from antiquity up to now for cultural heritage.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The μXRF measures have been obtained with the equipment acquired by the Interdepartmental Center “G. Scansetti” for Studies on Asbestos and Other Toxic Particulates with a grant from Compagnia di San Paolo, Torino, Italy. Special thanks are due to B. Fubini and R. Compagnoni (Center “G. Scansetti” UNITO) for encouragement and for providing μXRF. Financial support by the Italian Ministry of Instruction, University and Research and by the National Council of Research.
References
 J. Zöldföldi, B. Szekely, and Ch. Franzen, “Interdisciplinary data base of historically relevant marble material for archaeometric, art historian and restoration use,” in Natursteinsanierung Stuttgart 2004, Neue Natursteinsanierungsergebnisse und Messtechnische Erfassungen, G. GrasseggerSchön and G. Patitz, Eds., pp. 79–86, Siegl, Munchen, Germany, 2004. View at: Google Scholar
 H. Craig and V. Craig, “Greek marbles: determination of provenance by isotopic analysis,” Science, vol. 176, no. 4030, pp. 401–403, 1972. View at: Google Scholar
 N. Herz, “The oxygen and carbon isotopic data base for classical marble,” in ASMOSIA I: Classical Marble: Geochemistry, Technology, Trade, N. Herz and M. Waelkens, Eds., pp. 305–314, Kluwer Academic, Dodrecht, The Netherlands, 1988. View at: Google Scholar
 C. Gorgoni, L. Lazzarini, P. Pallante, and B. Turi, “An updated and detailed mineropetrographic and CO stable isotopic reference database for the main Mediterranean marbles used in antiquity,” in ASMOSIA V: Interdisciplinary Studies on Ancient Stone, J. J. Hermann, N. Herz, and R. Newton, Eds., pp. 115–131, Archetype Publications, London, UK, 2002. View at: Google Scholar
 D. Attanasio, M. Brilli, and N. Ogle, The Isotopic Signature of Classical Marbles, L'Erma di Bretschneider, Roma, Italy, 2006.
 V. Barbin, K. Ramseyer, D. Decrouez, S. V. Burns, and J. L. Mayer, “Cathodoluminescence of white marbles: an overview,” Archaeometry, vol. 43, pp. 175–183, 1992. View at: Publisher Site  Google Scholar
 J. J. Herrmann Jr. and V. Barbin, “The exportation of marble from the Aliki Quarries on Thasos: cathodoluminescence of samples from Turkey and Italy,” The American Journal of Archaeology, vol. 97, pp. 91–103, 1993. View at: Publisher Site  Google Scholar
 E. Mello, D. Monna, and M. Oddone, “Discriminating sources of mediterranean marbles: a pattern recognition approach,” Archaeometry, vol. 30, pp. 102–108, 1988. View at: Publisher Site  Google Scholar
 M. Unterwurzacher, J. Polleres, and P. Mirwald, “Provenance study of marble artefacts from the Roman burial area of Faschendorf (Carinthia, Austria),” Archaeometry, vol. 47, no. 2, pp. 265–273, 2005. View at: Publisher Site  Google Scholar
 V. Perdikatsis, K. Kritsotakis, T. Markopoulos, and K. Laskaridis, “Discrimination of Greek marbles by trace, isotope and mineralogical analysis,” in Fracture and Failure of Natural Building Stones, S. K. Kourkoulis, Ed., pp. 497–515, Springer, Dordrecht, Germany, 2006. View at: Google Scholar
 D. Attanasio, Ancient White Marbles: Analysis and Identification by Paramagnetic Resonance Spectroscopy, L'Erma di Bretschneider, Roma, Italy, 2003.
 N. Herz, D. G. Mose, and D. B. Wenner, “A possible discriminant for classical marble provenance,” Geological Society of America Abstracts With Programs, vol. 14, p. 514, 1982. View at: Google Scholar
 M. Brilli, G. Cavazzini, and B. Turi, “New data of 87Sr/86Sr ratio in classical marble: an initial database for marble provenance determination,” Journal of Archaeological Science, vol. 32, no. 10, pp. 1543–1551, 2005. View at: Publisher Site  Google Scholar
 D. Taelman, M. Elburg, L. Lopes, and P. de Paepe, “Quarrying the Estremoz marbles (portugal): archeological, petrographical and geochemical characterization,” ASMOSIA Abstracts, vol. 2012, pp. 235–236, 2012. View at: Google Scholar
 L. Lazzarini, G. Moschini, and B. M. Stievano, “A contribution to the identification of italian, greek and anatolian marbles through a petrological study and the evaluation of Ca/Sr ratio,” Archaeometry, vol. 22, no. 2, pp. 173–182, 1980. View at: Publisher Site  Google Scholar
 L. Rybach and H. U. Nissen, “Neutron activation of Mn and Na traces in marbles worked by the ancient Greeks,” in Proceedings of the Radiochemical Methods of Analysis, pp. 105–117, Salisburgo, Austria, 1965. View at: Google Scholar
 M. Oddone, S. Meloni, and E. Mello, “Provenance studies of the white marble of the cathedral of Como by neutron activation analysis and data reduction,” Journal of Radioanalytical and Nuclear Chemistry, vol. 90, no. 2, pp. 373–381, 1985. View at: Publisher Site  Google Scholar
 A. P. Grimanis and M. VassilakiGrimani, “Provenance studies of greek marbles by instrumental neutron activation analysis,” in Classical Marble: Geochemistry, Technology, Trade, N. Herz and M. Wealkens, Eds., vol. 153 of NATO ASI Series, pp. 275–281, Kluwer Academic, 1988. View at: Google Scholar
 L. Moens, P. Roos, J. de Rudder, and J. Hoste, “Archaeometry,” in Proceedings of the 25th International Symposium on Archaeometry, Y. Maniatis, Ed., Elsevier, Amsterdam, The Netherlands, 1989. View at: Google Scholar
 A. Borghi, G. Vaggelli, C. Marcon, and L. Fiora, “The piedmont white marbles used in antiquity: an archaeometric distinction inferred by a mineropetrographic and co stable isotope study,” Archaeometry, vol. 51, no. 6, pp. 913–931, 2009. View at: Publisher Site  Google Scholar
 K. J. Matthews, “The establishment of a data base of neutron activation analyses of white marble,” Archaeometry, vol. 39, no. 2, pp. 321–332, 1997. View at: Google Scholar
 D. Attanasio, G. Armiento, M. Brilli, M. C. Emanuele, R. Platania, and B. Turi, “Multimethod marble provenance determinations: the Carrara marbles as a case study for the combined use of isotopic, electron spin resonance and petrographic data,” Archaeometry, vol. 42, no. 2, pp. 257–272, 2000. View at: Google Scholar
 D. Attanasio, R. Platania, and P. Rocchi, “The marble of the David of Michelangelo: a multimethod analysis of provenance,” Journal of Archaeological Science, vol. 32, no. 9, pp. 1369–1377, 2005. View at: Publisher Site  Google Scholar
 K. Polikreti and Y. Maniatis, “A new methodology for the provenance of marble based on EPR spectroscopy,” Archaeometry, vol. 44, no. 1, pp. 1–21, 2002. View at: Google Scholar
 K. Polikreti, Y. Maniatis, Y. Bassiakos, N. Kourou, and V. Karageorghis, “Provenance of archaeological limestone with EPR spectroscopy: the case of the Cypriotetype statuettes,” Journal of Archaeological Science, vol. 31, no. 7, pp. 1015–1028, 2004. View at: Publisher Site  Google Scholar
 S. Capedri and G. Venturelli, “Accessory minerals as tracers in the provenancing of archaeological marbles, used in combination with isotopic and petrographic data,” Archaeometry, vol. 46, no. 4, pp. 517–536, 2004. View at: Publisher Site  Google Scholar
 A. Ebert, E. Gnos, K. Ramseyer et al., “Provenance of marbles from Naxos based on microstructural and geochemical characterization,” Archaeometry, vol. 52, no. 2, pp. 209–228, 2010. View at: Publisher Site  Google Scholar
 G. Vaggelli and R. Cossio, “μXRF analysis of glasses: aA nondestructive utility for cultural heritage applications,” Analyst, vol. 137, no. 3, pp. 662–667, 2012. View at: Publisher Site  Google Scholar
 M. Serra, A. Borghi, L. M. Gallo, R. Hovoric, and G. Vaggelli, “Petrographic features, genesis and provenance of Pietra Paesina collections of the Regional Museum of Natural Sciences of Turin, Italy,” Periodico di Mineralogia, no. 95–111, 2010. View at: Publisher Site  Google Scholar
 M. Serra, A. Borghi, R. Cabella, L. M. Gallo, and G. Vaggelli, “MicroXRF trace element quantification in calcite: a contribution to white marble provenance determination,” Microscopy & Microanalysis, vol. 17, supplement S2, pp. 1808–1809, 2011. View at: Publisher Site  Google Scholar
 G. V. Dal Piaz, “The AustroalpinePiedmont nappe stack and the puzzle of Alpine Tethys,” Memorie di Scienze Geologiche, vol. 51, no. 1, pp. 155–176, 1999. View at: Google Scholar
 G. Chiari, F. Colombo, R. Compagnoni et al., “Santa Cristina: restoration of a facade by Filippo Juvarra,” in Proceedings of the International Congress on Deterioration and Conservation of Stone, pp. 1393–1402, Lisbon, Portugal, June 1992. View at: Google Scholar
 H. J. LucasTooth and C. Pyne, “The accurate determination of major constituents by Xray fluorescent analysis in the Presence of large interelement effects,” Advances in Xray Analysis, vol. 1964, no. 7, pp. 523–541, 1964. View at: Google Scholar
 N. Herz and N. E. Dean, “Stable isotopes and archaeological geology: the Carrara marble, northern Italy,” Applied Geochemistry, vol. 1, no. 1, pp. 139–151, 1986. View at: Google Scholar
 L. Moens, P. Roos, J. de Rudder et al., “White marble from Italy and Turkey: an archaeometric study based on minorand traceelement analysis and petrography,” Journal of Radioanalytical and Nuclear Chemistry, vol. 123, no. 1, pp. 333–348, 1988. View at: Publisher Site  Google Scholar
 P. Mirti, M. Aceto, L. Appolonia, and G. Bortolaso, “The provenance of white marbles from Roman buildings in the town of Aosta (Italy),” Science and Technology for Cultural Heritage, vol. 6, pp. 43–55, 1997. View at: Google Scholar
 P. A. Baker, J. M. Gieskes, and H. Elderfield, “Diagenesis of carbonates in deepsea sediments–evidence from Sr/Ca ratios and interstitial dissolved Sr data,” Journal of Sedimentary Petrology, vol. 52, no. 1, pp. 71–82, 1982. View at: Google Scholar
 M. J. Malone and P. A. Baker, “Temperature dependence of the strontium distribution coefficient in calcite: an experimental study from 40° to 200° and application to natural diagenetic calcites,” Journal of Sedimentary Research, vol. 69, no. 1, pp. 216–223, 1999. View at: Google Scholar
 E. W. M. Heinrich, Microscopic Petrography, McGrawHill, New York, NY, USA, 1956.
 M. G. Best, Igneous and Metamorphic Petrology, W. H. Freeman, San Francisco, Calif, USA, 1982.
 H. Craig, “Isotopic standards for carbon and oxygen and correction factors for massspectrometric analysis of carbon dioxide,” Geochimica et Cosmochimica Acta, vol. 12, no. 12, pp. 133–149, 1957. View at: Publisher Site  Google Scholar
 J. M. McCrea, “On the isotopic chemistry of carbonates and a paleotemperature scale,” The Journal of Chemical Physics, vol. 18, no. 6, pp. 849–857, 1950. View at: Google Scholar
 T. W. Schmid and S. M. Bernasconi, “An automated method for 'clumpedisotope' measurements on small carbonate samples,” Rapid Communications in Mass Spectrometry, vol. 24, no. 14, pp. 1955–1963, 2010. View at: Publisher Site  Google Scholar
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Copyright © 2014 Gloria Vaggelli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.