Nonpolynomial approximation of background in X-ray spectra

Kajfosz, J.; Kwiatek, Wojciech M.

doi:10.1016/0168-583x(87)90298-9

Cited by 27 publications

(8 citation statements)

References 4 publications

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“…Basically, it consists of a linear or non-linear intensity addition which results in a distorted measurement, whereby the observed value is higher than its real value. Multiple algorithms have been proposed to deal with the estimation and correction of baseline effects, [23][24][25][26][27][28][29][30] and the fundamentals of operation of these baseline methods is beyond the scope of this paper. After a series of preliminary experiments, it was decided to use the ALS algorithm [23] which offered the best results for the spectral data recorded in this work.…”

Section: Baselinementioning

confidence: 99%

Machine learning algorithms applied to Raman spectra for the identification of variscite originating from the mining complex of Gavà

Díez-Pastor

Jorge‐Villar

Arnaiz‐González

et al. 2018

J Raman Spectroscopy

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Variscite is an aluminium phosphate mineral widely used as a gemstone in antiquity. Knowledge of the ancient trade in variscite has important implications on the historical appreciation of the commercial and migratory movements of human population. The mining complex of Gavà, which dates from the Neolithic, is one of the oldest underground mine sites in Europe, from where variscite was extracted from several mines and at different depths, providing minerals with different properties and a range of colours. In this work, machine learning algorithms have been used to classify variscite samples from Gavà with regard to the identification of their mine of origin and extraction depth. The final objective of the study was to see if the Raman spectroscopic signatures selected by these algorithms had a key spectral significance related to mineral structure and/or composition and validate the use of these computational procedures as a useful tool for detecting variances in the mineral Raman spectra that could facilitate the assignment of the specimens to each mine.

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Section: Baselinementioning

confidence: 99%

Machine learning algorithms applied to Raman spectra for the identification of variscite originating from the mining complex of Gavà

Díez-Pastor

Jorge‐Villar

Arnaiz‐González

et al. 2018

J Raman Spectroscopy

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“…Wavelet transform 12 was used to filter out the baseline in open-path FT-IR spectra (OP/FT-IR). Kajfosz and Kwiatek 13 modeled the baseline in X-ray spectra, such as from XRF, PIXE, and SRIXE, using the envelope of parabolas placed above and below the spectrum. The rolling ball method 14 and the correction method presented in Parente 15 are similarly based on fitting the envelope of ellipses and Hermite polynomials, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In the past two decades, many automated solutions to the baseline removal problem have been proposed for different applications. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] Commonly, these methods have multiple adjustable parameters that must be tuned to the user's specific data and task. Identifying their optimal values is often a time-consuming process.…”

Section: Introductionmentioning

confidence: 99%

A Fully Customized Baseline Removal Framework for Spectroscopic Applications

et al. 2017

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The task of proper baseline or continuum removal is common to nearly all types of spectroscopy. Its goal is to remove any portion of a signal that is irrelevant to features of interest while preserving any predictive information. Despite the importance of baseline removal, median or guessed default parameters are commonly employed, often using commercially available software supplied with instruments. Several published baseline removal algorithms have been shown to be useful for particular spectroscopic applications but their generalizability is ambiguous. The new Custom Baseline Removal (Custom BLR) method presented here generalizes the problem of baseline removal by combining operations from previously proposed methods to synthesize new correction algorithms. It creates novel methods for each technique, application, and training set, discovering new algorithms that maximize the predictive accuracy of the resulting spectroscopic models. In most cases, these learned methods either match or improve on the performance of the best alternative. Examples of these advantages are shown for three different scenarios: quantification of components in near-infrared spectra of corn and laser-induced breakdown spectroscopy data of rocks, and classification/matching of minerals using Raman spectroscopy. Software to implement this optimization is available from the authors. By removing subjectivity from this commonly encountered task, Custom BLR is a significant step toward completely automatic and general baseline removal in spectroscopic and other applications.

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“…Induced X-rays were measured with the ORTEC Si(Li) semiconductor detector with resolution c. 200 eV. The data reduction was performed witb a software package developed for thick target analysis Kajfosz and Kwiatek, 1987).…”

Section: Materials and Methods Of Investigationmentioning

confidence: 99%

“…The resolution of the detector at 6.4 keV was c. 170 eV. Emission spectra were processed using the least-squares method of with backgrounds cut using nonpolynomial approximation (Kajfosz and Kwiatek, 1987). 30 EPMA measurements were used to calibrate against an internal Fe standard.…”

Section: Au and Pge In Black Shale 105mentioning

confidence: 99%

EPMA, micro-PIXE, synchrotron microprobe and TEM study of visible and invisible accumulations of Au and PGE in black shale and organic matrix, Kupferschiefer, Poland

Kucha¹,

Przyblowicz

Lankosz

et al. 1993

Mineral. mag.

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The Kupferschiefcr contains from 0.3 to 30 wt.% of organic matter, with an average value close to 10 wt.%. The concentration of heavy metals increases strongly towards the base of a black shale horizon. Where the black shale rests directly on red sandstone, the bottom few mm of the shale may contain (ppm): Hg 8-1500, mean 174; Ag 310-5780, mean 1222; Au 1-3000, mean 40; Pd 2-1000, mean 37; Pt 2-340, mean 18. The shale, enriched in noble metals, is overlain by thucholite shale. The flotation sulphide concentrate contains 1-3 ppm Au and a detectable Pd and Pt content. Selected gravity concentrates from the black shale contain 4-56 ppm Au, up to 25 ppm Pd, while the Pt content does not exceed 1 ppm. A tightly intermixed clay-organic matrix of the noble metal-bearing shale contains small accumulations of Au, Pt and Pd presumably as organomctallic compounds.Thucholite is present locally. It contains inclusions of uraninite, brannerite and noble metal minerals lcoated at the boundary between optically isotropic and anisotropic components. The optically homogeneous organic matrix of the thucholite contains discrete inclusions of UO2, brannerite, electrum and organic compounds of Au, U, Pt and Ni varying in size from 0.008 to 0.025 ~tm as shown by TEM. Gold occurs in a continuous Au-Ag series. Refractory Au is present as substitutions in native silver and in the organic matrix of the shale and thucholite. Palladium forms several arsenides and sulphoarsenides. Refractory Pd is present as substitutions in Ni-Co arsenides and in the organic matrix of the shale and thucholite. Platinum is present as substitutions in the organic matrix of the noblemetal-bearing shale and thucholite.The average detection limits of micro-PIXE were 8-10 ppm, and those of the SXRFA from one to a few ppm. A major factor defining detection limits in the studied samples were minor uranium lines overlapping spectral lines of the studied elements.

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Nonpolynomial approximation of background in X-ray spectra

Cited by 27 publications

References 4 publications

Machine learning algorithms applied to Raman spectra for the identification of variscite originating from the mining complex of Gavà

Machine learning algorithms applied to Raman spectra for the identification of variscite originating from the mining complex of Gavà

A Fully Customized Baseline Removal Framework for Spectroscopic Applications

EPMA, micro-PIXE, synchrotron microprobe and TEM study of visible and invisible accumulations of Au and PGE in black shale and organic matrix, Kupferschiefer, Poland

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