Comparison of Handheld and Echelle Spectrometer to Assess Copper in Ores by Means of Laser-Induced Breakdown Spectroscopy (LIBS)

Abstract

Its properties make copper one of the world’s most important functional metals. Numerous megatrends are increasing the demand for copper. This requires the prospection and exploration of new deposits, as well as the monitoring of copper quality in the various production steps. A promising technique to perform these tasks is Laser Induced Breakdown Spectroscopy (LIBS). Its unique feature, among others, is the ability to measure on site without sample collection and preparation. In this work, copper-bearing minerals from two different deposits are studied. The first set of field samples come from a volcanogenic massive sulfide (VMS) deposit, the second part from a stratiform sedimentary copper (SSC) deposit. Different approaches are used to analyze the data. First, univariate regression (UVR) is used. However, due to the strong influence of matrix effects, this is not suitable for the quantitative analysis of copper grades. Second, the multivariate method of partial least squares regression (PLSR) is used, which is more suitable for quantification. In addition, the effects of the surrounding matrices on the LIBS data are characterized by principal component analysis (PCA), alternative regression methods to PLSR are tested and the PLSR calibration is validated using field samples.

Effect of Surface Condition on the Results of Chemical Composition Measurements of Scrap Copper Alloys

“…The advantage of the devices working with XRF technology is that there is no damage to the surface to be tested, so they are also used to analyze antique objects and those that must not be damaged [7][8][9]. In addition, the technology can be used to measure powders and geodesic materials [10][11][12][13]. Spectrometers operating in LIBS technology [14], due to the better detection of light elements (aluminium, magnesium, silicon, etc.…”

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

“…Yoon et al used LIBS and partial least squares regression (PLSR) method to analyze heavy metals in sediments [20]. Kashiwakura and Wagatsuma used LIBS and PLSR method to analyze austenitic stainless steel [21]. Brinkmann et al used laser-induced breakdown spectroscopy combined with PLSR method to determine copper in ores [22].…”