## 2 Application of Portable XRF for Ceramic Investigations: The State of the Art
On the other hand, if the comparability of the dataset to others could not be assured, solid empirical evidence proves the adequacy of the application of built-in calibration routines for obtaining internally consistent result for archaeological interpretations [35; 38]. The validation of dedicated internal calibrations in the analysis of ceramics represents an important challenge for making pXRF more accessible. For many users, a lack of access to more sensitive analytical techniques (e.g., ICP-OES, or NAA) for collecting calibration data for the creation of a matrix-matched calibration can be highly problematic and expensive, as can the acquisition of commercial calibration standards that are suitable for unprepared archaeological samples [24]. The recent advancements in hardware and software development for pXRF led to the release of more precise internal calibrations, and recent studies have called for an overall acceptance of specific built-in calibration algorithms for the semi-quantitative characterization of archaeological materials [3; 24; 28; 29; 38; 39]. Robust multivariate statistical methods have been widely and successfully applied to the analysis of large chemical databases for provenance studies and for the recognition and characterization of local ceramic production to identify possible trading activities [4; 25; 40; 41]. To better represent the high variability of a geochemical dataset, a pre-processing stage is thus required to standardize data and reduce distinctions in the magnitudes between major and trace elements [6; 42; 43]. While some recent studies have stressed the advantages of applying log ratio transformations to raw data [44; 45; 46; 47], the use of standardized log10 data, combined with multivariate statistical methods, has been demonstrated to satisfactorily represent the fluctuations in the absolute concentrations of a geochemical dataset [48; 49]. Qualitative pXRF analyses are frequently used in archaeology to determine the relative composition of a substance with the main aim of detecting distinguishable elements for further provenance studies [3; 36; 37; 38; 39]. A number of recent studies explored both the potentialities and limitations of the analysis of pottery using pXRF, which has thus been proven to be feasible tool for the investigation of individual assemblages of various ceramic waves [30; 50]. One of the most frequently highlighted limitations of the use of pXRF in provenance studies is that is has a more restricted range of detectable elements than traditional analytical methods. Despite the fact that the sensitivity and number of elements that can be measured by pXRF devices is between 10 and 20, only a variable range of 5-10 elements are in general found to be useful. Moreover, the distinction between different ceramic groups is generally based upon elements that XRF methods are not great at detecting (e.g., rare earth elements) [6]. More sensitive analytical methods (e.g., INAA, ICP-MS, etc.) have been so far extensively used in provenance studies because they can be used as part of a high dimensional dataset (for INAA and ICP-MS, this is 23 and 19 elements, respectively), which comprehensively incorporates part of the spectrum of rare earth elements (REE), meaning that they can precisely segregate elemental groups on a microregional scale [1; 51]. By contrast, most of these methods involve long analytical times, together with complex and destructive sample preparation. Even though the sample preparation issue could be solved by using laser ablation ICP-MS, its application to the analysis of heterogeneous materials is frequently semi-quantitative at best [35; 52]. Despite the non-negligible discrepancies that are registered in the absolute elemental concentrations, non-destructive pXRF has been demonstrated to be able to distinguish between geochemically different ceramic pastes and identify groups in ways that closely correlate with those indicated by traditional methods [19; 20; 21; 22; 23; 24].