Principal component analysis-assisted energy dispersive X-ray fluorescence spectroscopy for non-invasive quality assurance characterization of complex matrix materials

摘要

The analytical challenges in direct quality assurance analysis of complex matrices (extreme matrix effects, spectral overlap, poor signal-to-noise ratio (SNR) for trace analytes, 'dark matrix', imprecise geometry, need for sample integrity) by energy dispersive X-ray fluorescence (EDXRF) spectrometry necessitate development of novel techniques for material characterization. We demonstrate the utility of principal component analysis (PCA) in isotope-excited EDXRF spectrometry of a complex matrix (in this case lubricating oil) in the context of a newly developed EDXRF and scattering (EDXRFS) technique. Lubricating oil quality may be interpreted in terms of its viscosity, anti-wear, anti-oxidation, and anti-rust properties, which are detectable via B, Ca, Mg, Zn, Fe, Na additives (quality markers). Our method involves simultaneous non-invasive acquisition of both fluorescence and scatter spectra from samples held in a propylene dish, and their modeling in a reduced multidimensional space for an interpretable overview that is analytically more useful than, and complementary to, fluorescence peak-based quantitation of the additives; by this method, only Fe and Zn are directly detectable, but with SNR of the fluorescence peak 15-20 times poorer compared with analysis after sample digestion. Although Fe and Zn cannot distinguish the various lubricating oil brands, it can differentiate authentic from adulterated. The method was however found to be analytically useful when combined with PCA: various brands of lubricating oil were discriminated in addition to the detection of adulteration. PCA processing of the spectra showed that the most important quality assurance spectral signature information responsible for the success is contained in the scatter region (low-Z elements). Evaluation of the performance of the method with respect to SNR (i.e. analysis time and therefore speed) showed that there was no significant difference in method performance of analysis live time in the range 100-1000s, showing proof of concept for rapid characterization of complex matrix materials by PCA-assisted EDXRFS.