Application of chemometric methods to XRF-data – A tutorial review

Панчук, В. В.; Yaroshenko, Irina; Legin, Andrey; Семенов, В. Г.; Kirsanov, Dmitry

doi:10.1016/j.aca.2018.05.023

Cited by 110 publications

(74 citation statements)

References 127 publications

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“…Recent studies have proposed the use of more sophisticated methods for modelling XRF data (e.g., partial least squares regression (PLSR), machine learning and computational models) to deal with the matrix effect, as well as to account for and extract hidden spectral information [32,54]. These modelling approaches can be promising for optimizing XRF data processing, especially for modelling datasets with a large number of soil samples.…”

Section: Pxrf Models Performance For the Prediction Of Key Fertility mentioning

confidence: 99%

“…For example, in equipment with Rh-anode tube, the elastic (Thomson) and inelastic (Compton) scattering peaks appear between 18 and 22 keV. However, these scattered X-rays can also be important because they can be explored as a source of soil information [32]. The intensity of the XRF scattering energy is inversely related to the average atomic number of the sample, which can allow inferring about soil density and its related attributes (e.g., OM and texture).…”

Section: Introductionmentioning

confidence: 99%

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Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

et al. 2020

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The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R2) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples.

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Section: Pxrf Models Performance For the Prediction Of Key Fertility mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

et al. 2020

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“…In this study, a recent chemometric method for the classification of samples was applied. 15,25,26 Instead of using an algorithm that tests one by one, this method allows the combination of information from 17 classifiers with no requirements to select a threshold, eigenvector or number of neighbors. In addition, it is possible to easily fuse the data from different instruments.…”

Section: Resultsmentioning

confidence: 99%

Direct Analysis of Human Hair Before and After Cosmetic Modification Using a Recent Data Fusion Method

Santos¹,

Pereira²

2020

J. Braz. Chem. Soc.

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The cosmetic modification of hair is a very common procedure used to mask or cover evidence at a crime scene. Deoxyribonucleic acid (DNA) tests are expensive and require good-quality collection of samples and a database profile. To overcome these challenges, direct analysis was performed on a large set of hair strands collected from individuals, denoted original samples, and the data were compared with those of the same samples after cosmetic modification performed by bleaching the samples in the laboratory. A total of 127 samples were evaluated in this study using two analytical techniques, wavelength-dispersive X-ray fluorescence (WDXRF) and laser-induced breakdown spectroscopy (LIBS). Instead of testing many algorithms to develop classification models for the original and bleached samples, a recent method was applied that combines information from 17 classifiers. Data fusion was also evaluated to improve the accuracy of the classification model, which was higher than 99.2%, with no requirements to select eigenvectors or thresholds.

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“…Modern literature on the use of machine learning methods in chemical analysis (chemometrics) is, in general, quite extensive and diverse. In recent years, a large number of reviews have been published on individual methods and analyzed objects [123][124][125][126][127]. However, the number of studies using chemometric methods, against the background of the total number of analytical works, is still extremely small.…”

Section: Spectroscopic Data Processing Using Chemometric Methods For mentioning

confidence: 99%

Low-Input Crops as Lignocellulosic Feedstock for Second-Generation Biorefineries and the Potential of Chemometrics in Biomass Quality Control

et al. 2019

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Lignocellulose feedstock (LCF) provides a sustainable source of components to produce bioenergy, biofuel, and novel biomaterials. Besides hard and soft wood, so-called low-input plants such as Miscanthus are interesting crops to be investigated as potential feedstock for the second generation biorefinery. The status quo regarding the availability and composition of different plants, including grasses and fast-growing trees (i.e., Miscanthus, Paulownia), is reviewed here. The second focus of this review is the potential of multivariate data processing to be used for biomass analysis and quality control. Experimental data obtained by spectroscopic methods, such as nuclear magnetic resonance (NMR) and Fourier-transform infrared spectroscopy (FTIR), can be processed using computational techniques to characterize the 3D structure and energetic properties of the feedstock building blocks, including complex linkages. Here, we provide a brief summary of recently reported experimental data for structural analysis of LCF biomasses, and give our perspectives on the role of chemometrics in understanding and elucidating on LCF composition and lignin 3D structure.

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Application of chemometric methods to XRF-data – A tutorial review

Cited by 110 publications

References 127 publications

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

Direct Analysis of Human Hair Before and After Cosmetic Modification Using a Recent Data Fusion Method

Low-Input Crops as Lignocellulosic Feedstock for Second-Generation Biorefineries and the Potential of Chemometrics in Biomass Quality Control

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