## 3 EM Techniques for CH Diagnosis
### Infrared Spectroscopy (IRS) and Raman Spectroscopy in CH Applications
#### 3.7.3 Improved Quantitative Data Analysis and Case Studies in Art Restoration  
In the realm of material analysis and conservation science, the integration of quantitative data analysis has become increasingly vital. Techniques such as peak comparison of XRF patterns enable detailed characterization of materials, offering insights that complement traditional observational methods. Additionally, examining cases like the degradation of modern art plastics highlights the importance of combining analytical data with visual assessments to better understand material changes over time. By leveraging these approaches, researchers and conservators can enhance their ability to evaluate, preserve, and restore artifacts with greater precision and confidence. To further improve quantitative data analysis in this study, consider implementing the strategies reported in Table 19 [145].  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{3}{*}{\begin{tabular}{l} Peak Comparison of XRF \\ Patterns \\ \end{tabular} } & \(\bullet\) & **Quantitative Elemental Analysis:** Utilize peak intensity ratios (e.g., Fe/K, Ti/Si) to compare elemental compositions across samples. \\ \multirow{3}{*}{\begin{tabular}{l} Peak Comparison of XRF \\ Patterns \\ \end{tabular} } & \(\bullet\) & **Peak Area Integration:** Perform precise peak area calculations using software tools (such as PyMCA, QXAS, or Origin) to quantify elemental concentrations. \\ \multirow{3}{*}{\begin{tabular}{l} Case Studies—Modern \\ Art Plastic Degradation \\ \end{tabular} } & \(\bullet\) & **Software Analysis:** Apply statistical methods (e.g., Principal Component Analysis (PCA), cluster analysis) to identify similarities or differences in elemental profiles among samples or before and after restoration. \\ \hline \multirow{3}{*}{\begin{tabular}{l} Advanced-Data \\ Processing Techniques \\ \end{tabular} } & \(\bullet\) & **Multivariate Analysis:** Use multivariate statistical techniques to interpret complex spectral data and identify subtle differences. \\ \multirow{3}{*}{\begin{tabular}{l} Processing Techniques \\ \end{tabular} } & \(\bullet\) & **Standardization and Calibration:** Employ calibration standards to convert XRF intensities into absolute concentrations, enabling more accurate comparisons. \\ \hline \multirow{3}{*}{\begin{tabular}{l} Case Studies—Modern \\ Art Plastic Degradation \\ \end{tabular} } & \(\bullet\) & **Before and After Restoration Comparison:** Quantify changes in degradation products by analyzing specific marker peaks associated with plastic degradation (e.g., oxidation-related elements or compounds). \\ \multirow{3}{*}{\begin{tabular}{l} Case Studies—Modern \\ Art Plastic Degradation \\ \end{tabular} } & \(\bullet\) & **Spectral Deconvolution:** Use spectral fitting methods to separate overlapping peaks, providing clearer insights into chemical changes. \\ \multirow{3}{*}{\begin{tabular}{l} Case Studies \\ \end{tabular} } & \(\bullet\) & **Correlate with Visual and Physical Data:** Combine quantitative spectral data with physical measurements (like mechanical testing or visual documentation) to assess restoration efficacy. \\ \hline \multirow{3}{*}{\
\begin{tabular}{l} Supplementary \\ Experimental Data \\ \end{tabular} } & \(\bullet\) & Incorporate complementary techniques such as Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, or Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDS) for a multi-modal quantitative approach. \\ \multirow{3}{*}{
\begin{tabular}{l} Experimental Data \\ \end{tabular} } & \(\bullet\) & Use time-resolved or in-situ measurements to monitor degradation or restoration processes dynamically \\ \hline \hline \end{tabular}
\end{table}
Table 18: _Cont._