## 2 Background
[10] used reflectance spectroscopy, XRFS, and LIBS to obtain quantitative mineralogical information on slurries from the tailings stream of a flotation plant of an iron ore concentrator needed for processes that control optimization during the ore beneficiation operation. It was observed that the on-stream mineralogical assaying of the slurry flows could be improved significantly by data fusion from the three analyzers. Data fusion performed using partial least squares regression enhanced the quantitative identification of hematite, magnetite, quartz and ferroichterite to provide a valid estimate of the slurry mineral content. More recently in 2019, and particularly apropos to this study, [PERSON] et al. [11] integrated the output of four instruments commonly used in soil studies - visible near-infrared (VIS-NIR) spectroscopy, mid-wavelength infrared (MWIR) spectroscopy, XRFS, and LIBS to achieve rapid measurement of soil properties. Rather than use full spectra, a genetic algorithm and partial least-squares regression (PLSR) analysis were used to select characteristic spectral bands. Models were then calibrated using PLSR for single analyzer data, PLSR for fused data combined from the four instruments, and Bayesian model averaging based on prediction results of fused data. For the single analyzer data, predictive performance decreased as follows: MWIR\(>\)VIS-NIR\(>\)LIBS\(>\)XRFS. The multianalyzer data fusion approach slightly improved or even reduced prediction accuracy and caused a large amount of redundancy, whereas the Bayesian model averaging achieved the best prediction performance of the six soil properties. A year later, [PERSON] et al. [12] employed a data fusion approach for integrating MWIR and long-wavelength infrared (LWIR) spectroscopy to facilitate the more accurate prediction elemental concentrations in a polymetallic sulfide deposit. Prediction models linking the mineralogical concentrations with the reflectance spectra were developed using PLSR, principal component regression (PCR) and support vector regression (SVR) methodologies, with results indicating that SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) concentrations in minerals could be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion. In that same year, [PERSON] [53] analyzed 147 Danish agricultural soils by LIBS and VIS-NIR spectroscopy for soil phosphorus determination. PLSR models were combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods to identify the relevant wavelengths for the study. This merging of LIBS and VIS-NIR spectroscopy with variable selection showed potential for improving soil phosphorus determination. Also in 2020, [PERSON] et al. [13] developed a data-driven approach based on LIBS and RS for the reliable identification of clay minerals in mixed-mineralogy specimens. A low-level data fusion approach was used to concatenate spectral datasets from the two spectroscopic techniques into a single data matrix that served as a unique identifier of specimen composition. Multivariate statistical analysis was used to identify the different clay mineralogical groups, with fused data classifying the samples based on their dominant clay mineralogy better than either the Raman and LIBS data separately. It is our hypothesis that there is much to be gained for geological applications through a multianalyzer system comprised of three technologies--XRFS, RS, and LIBS. Each of the three techniques for chemical analysis has certain common performance attributes, such as minimally-destructive analysis and rapid data collection and processing.