The use of UV‐VIS‐NIR reflectance spectroscopy to identify iron minerals

Szalai, Zoltán; Kiss, Klaudia; Jakab, Gergely; Sipos, Péter; Belucz, B.; Németh, Tibor

doi:10.1002/asna.201211965

Cited by 32 publications

(14 citation statements)

References 12 publications

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“…Such applications are usually qualitative. For example, near-infrared (NIR) sensors can provide accurate identification of clay minerals and rock-forming minerals [13,14], whereas short-wave infrared (SWIR) is one of the most widely used infrared technologies for the identification of alteration minerals [15,16]. On the other hand, LWIR permits identification of rock-forming minerals, whereas far-infrared (FIR) can be used for the identification of rare earth minerals [11,17].…”

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confidence: 99%

Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals

Desta

Buxton

Jansen

2020

Sensors

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Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions are proposed, aiming to facilitate more accurate prediction of SiO 2 , Al 2 O 3 , and Fe 2 O 3 concentrations in a polymetallic sulphide deposit. Two approaches of low-level data fusion were applied to these datasets. In the first approach, the pre-processed blocks of MWIR and LWIR data were concatenated to form a fused data block. In the second approach, a prior variable selection was performed to extract the most important features from the MWIR and LWIR datasets. The extracted informative features were subsequently concatenated to form a new fused data block. Next, prediction models that link the mineralogical concentrations with the infrared reflectance spectra were developed using partial-least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) analytical techniques. These models were applied to the fused data blocks as well as the individual (MWIR and LWIR) data blocks. The obtained results indicate that SiO 2 , Al 2 O 3 , and Fe 2 O 3 mineral concentrations can be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion; where the PLSR, PCR, and SVR models provided good and acceptable results. The proposed approach could be extended for online analysis of mineral concentrations in different deposit types. Thus, it would be highly beneficial in mining operations, where indications of mineralogical concentrations can have significant financial implications.for geometallurgical applications is not limited to knowledge on the grades of valuable elements and their variability, but also extends to the gangue minerals, as their composition and volume also play a crucial role in ore processing. Extant studies highlight the importance of mineralogical information for the sustainability and energy efficiency of geometallurgical processes [1,2]. Ore minerals occur in veins, disseminated in the host rock and/or in pores with varying concentrations of other associated minerals such as silica, oxides and carbonates. The concentration of these minerals can be associated with the metallurgical behaviour of the ore minerals. Therefore, quantitative mineralogical information on the co-occurring minerals is one of the crucial parameters for the optimisation of ore processing.Despite rapid advances in sensor technologies, there is still a demand for novel ideas to enable quantitative investigations of mineralogical compositions using sensor-derived data. In addition, in-situ application of sensor technologies requires portable and high-speed systems. Portable sensor technologies (such as X-ray fluorescence-XRF, and short-wave in...

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confidence: 99%

Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals

Desta

Buxton

Jansen

2020

Sensors

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“…For comparison of curves, Goodness of Fit Coe cient (GFC) [33] has been reported. GFC metric is calculated by the following equation (Eq.…”

Section: Goodness Tting Coe Cientmentioning

confidence: 99%

Identification of pigments in artworks by inverse tangent derivative of spectrum and a new filtering method

Fazlali

Kandi

2020

Preprint

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Employing an economical and non-destructive method for identifying pigments utilized in artworks is a significant aspect for preserving their antiquity value. One of the non-destructive methods for this purpose is spectrophotometry, which is based on the selected absorption of light. Mathematical descriptive methods such as derivatives of the reflectance spectrum, the Kubelka-Munk function and logarithm have been employed for the characterization of the peak features corresponding to the spectrophotometric data. In the present study, the mentioned mathematical descriptive methods were investigated with the aim to characterize the constituents of an Iranian artwork but were not efficient for the samples. Therefore, inverse tangent derivative equation was developed on spectral data for the first time, providing considerable details in the profile of reflectance curves. In the next part, to have a simpler and more practical method it was suggested to use filters made up of pure pigments. By using these filters and placing them on the samples, imaging was done. Then, images of samples with and without filter were evaluated and pure pigments were distinguished. The mentioned methods were also used to identify pigments in a modern Iranian painting specimen. The results confirmed these methods with reliable answers indicating that physical methods (alongside chemical methods) can also be effective in determining the types of pigments.

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“…In some cases, the accurate identification of the pigment becomes difficult due to the similarity of the reflection spectra of database and the sample. In order to increase the peak intensity and clarify its details, the derivation and logarithm of the reflection spectrum and the Kubelka-Munk theory are taken into account [20][21][22]. Many researchers studied in Kubelka-Munk space, including Berns, who worked on the mixture of linear combinations of pure pigments [23] and then Dupuis and Menu, who investigated mixing pigments with different concentrations in organic binders [14].…”

Section: Introductionmentioning

confidence: 99%

Identification of pigments in artworks by inverse tangent derivative of spectrum and a new filtering method

Fazlali

Kandi

2020

Herit Sci

View full text Add to dashboard Cite

Employing an economical and non-destructive method for identifying pigments utilized in artworks is a significant aspect for preserving their antiquity value. One of the non-destructive methods for this purpose is spectrophotometry, which is based on the selected absorption of light. Mathematical descriptive methods such as derivatives of the reflectance spectrum, the Kubelka–Munk function and logarithm have been employed for the characterization of the peak features corresponding to the spectrophotometric data. In the present study, the mentioned mathematical descriptive methods were investigated with the aim to characterize the constituents of an Iranian artwork but were not efficient for the samples. Therefore, inverse tangent derivative equation was developed on spectral data for the first time, providing considerable details in the profile of reflectance curves. In the next part, to have a simpler and more practical method it was suggested to use filters made up of pure pigments. By using these filters and placing them on the samples, imaging was done. Then, images of samples with and without filter were evaluated and pure pigments were distinguished. The mentioned methods were also used to identify pigments in a modern Iranian painting specimen. The results confirmed these methods with reliable answers indicating that physical methods (alongside chemical methods) can also be effective in determining the types of pigments.

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The use of UV‐VIS‐NIR reflectance spectroscopy to identify iron minerals

Cited by 32 publications

References 12 publications

Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals

Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals

Identification of pigments in artworks by inverse tangent derivative of spectrum and a new filtering method

Identification of pigments in artworks by inverse tangent derivative of spectrum and a new filtering method

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