## 5 Summary and Future Prospects  
In this study, we analyzed 129 soil samples gathered from multiple locations in the United States using X-ray fluorescence spectroscopy, Raman spectroscopy, and laser-induced breakdown spectroscopy to evaluate the benefits of a data fusion approach for the compositional discrimination of geological materials. The data was obtained using commercially available, handheld chemical analyzers and subsequently processed using principal component analysis and partial least squares discriminant analysis. Classification models were built for individual instruments and the resulting confidences were combined and used as the input features for the data fusion classifier. The classification accuracy was evaluated utilizing leave-one-sample-out cross-validation. The basis for classification was assessed and found to be consistent with the presence of elements and species typically encountered in soil.  
Figure 9: Probability density functions (pdfs) for the PLSDA regression weights applied to the features for the RS, XRFS, and LIBS analyzers for the New Mexico soils (top) and military installation soils (bottom). The absolute value was taken of the regression weights since the magnitude, positive or negative, indicates degree of influence on the classification decision. The weights were also normalized to place the pdfs for the two data sets on the same scale.  
Our results, presented above for the two largest soil data sets, suggest the potential for combining the data from multiple analyzers to improve soil classification/discrimination. However, for both data sets, classification with the LIBS data alone provided \(>\)90% accuracy. The XRFS results were similarly very good, whilst classification accuracy based solely on the use of Raman spectra was notably poorer because RS observes molecular character by contrast to the elemental composition detected by LIBS and XRFS. Application of the three-analyzer/data fusion approach to other geological materials, such as a suite of minerals in which at least some samples display more distinctive Raman bands and less fluorescence than soil, is necessary to better judge the utility of this methodology. In general, additional testing with data sets for which classification with individual analyzers is poor might provide greater insight into the value and limits of data fusion for improving classification accuracy through error correction. While data fusion appears robust even though there was poor discrimination for one of the individual analyzers, as observed for the Belmont soil pit discrimination results, an assessment of data fusion for poor performance by one or more analyzers would inform the minimum level of performance necessary with each instrument in order to benefit from fusion of their respective spectral data streams.  
All authors contributed to the effort, with contributions as follows: Conceptualization and Writing--R.R.H., C.S.T., & R.S.H.; Methodology--R.R.H. & C.S.T.; Sample Acquisition--J.M.H.H., J.B.H., J.L.C., R.S.H., R.R.H., & J.R.P.; Field and Laboratory Analysis--R.R.H., R.S.H., J.R.P., K.A.H., J.B.H., J.M.H., & J.L.C. Data analysis & Visualization--C.S.T.; Project Administration--J.R.P.; and Funding Acquisition--J.R.P., R.S.H., & R.R.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the USACE Engineer Research & Development Center under contract number W913E518C0011 of 20 September 2018.  
The authors declare no conflict of interest. The funding agency had no role in the design of the study, in the interpretation of data, or in the decision to publish the results.