## 2 Background
The X-ray method is well suited for analysis of many elements and especially transition metals, but its sensitivity decreases substantially for lighter elements such as Si, Al, and Mg. By contrast, the laser-based approach can detect all elements (H through U) and is especially sensitive to light elements such as Li, Be, B, and Na, thus making it an ideal complement to XRFS. However, the limit of detection for commonly encountered elements such as S, Cl and Br and some metals is better for XRFS than for LIBS. In addition, compared to XRFS, LIBS analysis is faster (seconds versus minutes) and the analytical spot size is smaller thus providing better spatial resolution. While there is overlap of the elements each method can detect, the strengths of one technique offset the weaknesses of the other. RS analysis identifies vibrational bands associated with molecules and thereby gives a molecular-structural fingerprint for samples that is wholly different from the compositional profiles that LIBS and XRFS provide. While RS analysis has the disadvantage of possible fluorescence interference, and the fact that certain materials give a very weak Raman signal (poor scatterers), the advantage of obtaining both elemental and molecular information about a sample can be highly useful. RS also has the benefit that it can be used to distinguish materials with similar or even identical elemental compositions (e.g., crystallographic polymorphs). Thus, the fusion of data from a combination of these spectroscopic tools expands the discriminating capability of the chemometric analysis. Several previous studies have demonstrated the benefit of two-analyzer fusion. For example, [PERSON] et al. [52] evaluated data fusion approaches for the combination of micro-Raman and micro-X-ray fluorescence data for the characterization of ochre pigments (hematite, caput mortum violet, yellow ochre, red ochre and burnt sienna) in cultural heritage objects. The classification of ochre pigments was improved using low-level fusion, with PCA and PLSDA models developed from the fused data observed to be more robust for prediction of new samples with respect to those results obtained by single instrument classification. [PERSON] and [PERSON] [23] utilized the capability of LIBS for elemental analysis and that of RS for molecular analysis to combine spectral outputs for the analysis and discrimination of explosive compounds. After simple mathematical treatment, image analysis of 2-D patterns was observed to be effective in discrimination of explosive materials from common confusants. [PERSON] et al. [7] used XRFS and RS for the analysis of art objects and described a data fusion procedure that was applied for the identification of the pigments used in porcelain cards from a database containing the fused spectra of 24 reference pigments. Classification by multivariate statistical analysis using the fused data was superior to that based on either the XRFS or RS data separately. In 2011, [PERSON] et al. [8] used spectral data from handheld XRFS and RS instruments to analyze pigments on over 80 fresco samples on-site at the Coriglia, Castel Viscardo archaeological site near Orvieto, Italy. Identified pigments included vermilion, red ochre, yellow ochre, terre verte, Egyptian blue, and hematite. Fusion of complete XRFS and RS spectra was observed to provide better sample discrimination than the use of individual spectral peaks. In a forensic study of printer inks,Hoehse et al. [24] fused RS and LIBS spectral data for ten blue and black ink samples on white paper into a single data matrix, with the number of different groups of inks then determined through multivariate analysis. In all cases, the results obtained from the integration of the RS and LIBS spectra were found to outperform the classification performance obtained with the individual RS or LIBS data sets. Subsequently, in 2017, [PERSON] et al.