# FUXYA2020: A Low-Cost Homemade Portable EDXRF Spectrometer for Cultural Heritage Applications
## 3 FUXYA2020 Performance on Real Cases in Cultural Heritage Field
### Ceramics  
A semi-quantitative test on archaeological ceramics samples has also been done, verifying the possibility of using the FUXYA spectrometer for archaeological material classification. Eight Roman amphorae were considered, of which the factory, and thus the production place, were known from archaeological information, such as seals, as summarized in Table 4. No archaeological consideration will be made in this work, as this research is still in progress and we are now interested only in the FUXYA's performance, but it is worth noting that we chose a small group with the same local provenance, and a few samples of various non-local provenance to have an easy test for our set up.  
We used the unfiltered continuous radiation from the X-ray tube and semi-quantitative evaluation, avoiding the determination of element concentrations [6].  
The XRF spectra were acquired on fractures and/or cleaned areas; where necessary, cleaning was performed with pure water on some cotton wool to eliminate visible earth traces. A variable number of measuring points was considered for each sample to check the homogeneity of the artifacts [52]; for a few samples, Cr (_7_3), Cu (_7_1, _17_4) or Rb (_9_3) showed peaks below the detection limit (see Section 3.1); in these few cases, to perform statistical analyses, we substituted the missing data with a random value between 0 and the detection limit itself [53; 8]. All the 22 spectra were then considered for statistical analysis on closed data. Statistical elaboration was obtained by using R 3.6.3 [54], a free environment for statistical computing and graphics. It compiles and runs on UNIX platforms, Windows and MacOS. In particular, the FactoMineR 2.4 [55] and pca3d 0.10.2 [56] packages have been used. Data elaboration was performed once again in the perspective of a low-cost project, requiring no expensive licenses. PCA was applied, exploiting the correlation matrix as relative concentrations have different scales and we wanted to standardize data; relative concentration was closed to 100% for multivariate analyses and classification to eliminate the differences among samples due to different silicate presence or firing temperatures, which could induce a varying weight loss also in samples with similar raw materials. This procedure is particularly advisable whenever samples contain indefinite amounts of extraneous material, such as temper (i.e., crushed shell or crushed stone) [8; 57].  
The working conditions were 40 kV and 0.06 mA with an acquisition time of 200 s for each measurement. The detector proprietary software was used to perform peak deconvolution and calculate areas.  
In Figure 10, the loading plot for the first two PCs is reported: even if the cumulative variance is relatively low (61.2%) and the samples are few, the various groups are well separated and allow the discrimination between different provenance which occupies  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **N. of Spectra** & **Archaeological Classification** \\ \hline
4 & 3 & Non-local (NA) \\
5 & 3 & Non-local (MA) \\
7 & 3 & Non-local (AA) \\
9 & 3 & Non-local (BA) \\
14 & 2 & local \\
15 & 2 & local \\
16 & 2 & local \\
17 & 4 & local \\ \hline \end{tabular}
\end{table}
Table 4: Analysed ceramics.  
different areas in the PCs cartesian plane. In fact, the local samples (14, 15, 16, 17) were grouped together, well-spaced from the non-local samples.