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

Abstract

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 100-1000 s, showing proof of concept for rapid characterization of complex matrix materials by PCA-assisted EDXRFS.

Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF

“…The relation between the original and the new variables is expressed through the so-called loadings. A loading plot shows which properties of the samples, in this case elements, define the principal components and thus, which elements in the soil matrix show the greatest variance within the data set 12 . The first principal component should only be related to relevant fluorescence peaks allowing the sorting of the samples 34 .…”

“…As conventional routine analysis for determination of nutrients in soils, wet digestion followed by measurement with inductively-coupled plasma optical emission spectrometry/-mass spectrometry (ICP-OES/ICP-MS) is used 9,10 . Disadvantages such as time-consuming sample preparation, possible contamination of chemical reagents and loss of volatile analytes through heating the sample solution may occure 11,12 . It has to be considered that XRF provides total contents while the plant can only absorb plant available nutrients.…”

“…X-Ray fluorescence methods for detection of element specific radiation are already known as a fast, non-destructive multi-element spectroscopic technique and have been widely applied in a variety of fields, such as agricultural 13 and geological 14 applications or environmental 12,15 monitoring. EDXRF has already been used for the analysis of trace elements as well as major and minor nutrients 16 in soils from different countries such as contaminated soils in the UK 17 , clayey and sandy soils from Denmark 18 or heavy trace elements in soils from Australia 19 with univariate calibration.…”

Study of diffraction data sets using factor analysis: a new technique for comparing mineralogical and geochemical data and rapid diagnostics of the mineral composition of large collections of rock samples

“…These methods serve to solve a wide range of tasks, e.g. : (1) analyzing large (thousands of spectra) datasets and increasing the efficiency of data collection (Kirian et al ., 2011; Angeyo et al ., 2012; Voronov et al ., 2014; Palin et al ., 2015; Guccione et al ., 2018); (2) determining the relations between developments in XRD data and other systematically changing properties (strength, viscosity, and volume) (Westphal et al ., 2015); (3) estimating the number of phases in the system and identifying each phase (Artyushkova and Fulghum, 2001; Caliandro et al ., 2013; Manceau et al ., 2014); (4) reducing the influence of the signal-to-noise ratio (Sastry, 1997; Schmidt et al ., 2003; Chen et al ., 2005; Walton and Fairley, 2005); (5) investigating the orientation and morphology of crystalline phases (Matos et al ., 2007); (6) tracking the development of complex processes of compositional and structural alterations in a multicomponent system (Westphal et al ., 2015); (7) performing the analysis of time-resolved experimental data (Schmidt et al ., 2003; Mabied et al ., 2014); (8) extracting kinetic information (the activation energy, the frequency factor, the reaction order, etc.) (Palin et al ., 2016; Guccione et al ., 2018).…”

Application of principal component analysis for improvement of X-ray fluorescence images obtained by polycapillary-based micro-XRF technique

“…In addition, PCA has been used for Raman imaging by researchers at Advanced Industrial Science and Technology (AIST) [5]. Not only imaging method, PCA has been applied in various spectrometry methods as traditional statistic technique [6,7].…”