Near infrared reflectance spectroscopy as a process signature in uranium oxides

Plaue, Jonathan; Klunder, Gregory L.; Hutcheon, I. D.; Czerwinski, Ken

doi:10.1007/s10967-012-2027-0

Cited by 16 publications

(18 citation statements)

References 17 publications

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“…Two milestones are well worth being mentioned in the mining industry context. The first one was the emergence of laboratory and field hyperspectral acquisition equipment, such as LabSpec [5][6][7], TerraSpec [24,25] and Hylogger [26] that are single pixel devices, and CoreScan [4] which is the most well-known multi-pixel laboratory platform. Additionally, there are diverse models of hyperspectral cameras [26]-most of them push broom style scanners-that are multi-pixel devices, for which the companies SPECIM [27][28][29][30][31] and Headwall [32,33] have established the industry standard.…”

Section: Laboratory and Field Based Geological Hyperspectral Analysismentioning

confidence: 99%

“…Expression (5) represents the concept of combining a spectral distance d S (x, y) with a spatial distance d R (x, y). Note that when m = 1, this combination depends on the uniform grid step s only-that is to say, if k increases, the spectral distance component importance increases.…”

Section: Wavelet Approachmentioning

confidence: 99%

“…Mining industry has taken advantage of near infrared (NIR) spectrography [1] technology to characterize geological and metallurgical samples. They record drill core trays using hyperspectral single pixel scanners, such as Hylogger [2], or 2D imaging systems such as Corescan [3,4], and they acquire spectra from composites of drill core segments using single pixel hyperspectral instruments such as ASD SpecLab [5][6][7]. In the first case, single pixel spectral analysis is used for semi-quantitative mineralogy, lithology and alteration characterization, supported by several mature unmixing techniques or linear regressions [8,9].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

et al. 2020

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Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach.

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Section: Laboratory and Field Based Geological Hyperspectral Analysismentioning

confidence: 99%

Section: Wavelet Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

et al. 2020

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“…Infrared (IR) reflectance spectroscopy, however, is considered to be an inexpensive technique that can rapidly produce data from a large number of samples in a timely and accurate manner. 7 Moreover, chemical characterization utilizing this technique has the potential to provide further insight into the manufacturing process and possibly even the origin of the sample. 3,[8][9][10][11] There have been some prior studies that characterize uranium minerals and chemicals using spectroscopic methods.…”

Section: Introductionmentioning

confidence: 99%

Identification of Uranium Minerals in Natural U-Bearing Rocks Using Infrared Reflectance Spectroscopy

et al. 2017

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The identification of minerals, including uranium-bearing species, is often a labor-intensive process using X-ray diffraction (XRD), fluorescence, or other solid-phase or wet chemical techniques. While handheld XRD and fluorescence instruments can aid in field applications, handheld infrared (IR) reflectance spectrometers can now also be used in industrial or field environments, with rapid, nondestructive identification possible via analysis of the solid's reflectance spectrum providing information not found in other techniques. In this paper, we report the use of laboratory methods that measure the IR hemispherical reflectance of solids using an integrating sphere and have applied it to the identification of mineral mixtures (i.e., rocks), with widely varying percentages of uranium mineral content. We then apply classical least squares (CLS) and multivariate curve resolution (MCR) methods to better discriminate the minerals (along with two pure uranium chemicals UO and UO) against many common natural and anthropogenic background materials (e.g., silica sand, asphalt, calcite, K-feldspar) with good success. Ground truth as to mineral content was attained primarily by XRD. Identification is facile and specific, both for samples that are pure or are partially composed of uranium (e.g., boltwoodite, tyuyamunite, etc.) or non-uranium minerals. The characteristic IR bands generate unique (or class-specific) bands, typically arising from similar chemical moieties or functional groups in the minerals: uranyls, phosphates, silicates, etc. In some cases, the chemical groups that provide spectral discrimination in the longwave IR reflectance by generating upward-going (reststrahlen) bands can provide discrimination in the midwave and shortwave IR via downward-going absorption features, i.e., weaker overtone or combination bands arising from the same chemical moieties.

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“…More recently, the application of infrared spectroscopy [7] and near infrared reflectance spectroscopy [8] to uranium ore concentrate samples has been reported. Applying all these techniques yields a set of parameters characteristic of the material and its history, often referred to as "signatures".…”

Section: Introductionmentioning

confidence: 99%

Applicability of Raman spectroscopy as a tool in nuclear forensics for analysis of uranium ore concentrates

et al. 2013

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In the present study, the suitability of Raman spectroscopy for nuclear forensics is proven by a systematic investigation of laboratory-prepared synthetic and industrial uranium ore concentrate (UOC) samples, of different origin and with wide range of impurity content. Besides the identification of the compound type, the identification of process-related impurities observed in these samples is reported for the first time. Both handheld and laboratory Raman spectrometers were used in the measurements. The ability to measure different UOCs and the sufficient sensitivity of the handheld device demonstrate its usefulness in nuclear safeguards and nuclear security applications. Even more importantly in such a context, the likely identity of process-related impurities could be verified by Raman Spectroscopy in the present work, allowing to draw conclusions on the process history of the sample.

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Near infrared reflectance spectroscopy as a process signature in uranium oxides

Cited by 16 publications

References 17 publications

A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

Identification of Uranium Minerals in Natural U-Bearing Rocks Using Infrared Reflectance Spectroscopy

Applicability of Raman spectroscopy as a tool in nuclear forensics for analysis of uranium ore concentrates

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