Quantification of multiple soil trace elements by combining portable X-ray fluorescence and reflectance spectroscopy

Shrestha, Gautam; Pereira, Roberto Calvelo; Roudier, Pierre; Martin, Adam P.; Turnbull, Rose; Keresztúri, Gábor; Jeyakumar, Paramsothy; Anderson, C. W.

doi:10.1016/j.geoderma.2021.115649

Cited by 14 publications

(4 citation statements)

References 62 publications

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“…Nevertheless, the OPA (CARS) estimation model shows the smallest RMSE (0.1661) and a relatively high R 2 (0.9515), which is consistent with Xu's research findings [67]. On the other hand, the estimation model constructed after using GRA for the decision-level fusion of spectral data demonstrates a more balanced accuracy, similar to the results obtained by Shrestha et al [71], but it requires a longer computational time (average computational time = 331). Therefore, the CVM, OPA, and GRA spectral fusion strategies each have their advantages in providing accurate, efficient, and stable methods for soil Pb content estimation models.…”

Section: Discussionsupporting

confidence: 89%

“…Therefore, both OPA and GRA strategies provide effective methods for using XRF and vis-NIR spectra in soil Pb content estimation. the decision-level fusion of spectral data demonstrates a more balanced accuracy, similar to the results obtained by Shrestha et al [71], but it requires a longer computational time (average computational time = 331). Therefore, the CVM, OPA, and GRA spectral fusion strategies each have their advantages in providing accurate, efficient, and stable methods for soil Pb content estimation models.…”

Section: Spectral Fusion Strategiessupporting

confidence: 83%

See 1 more Smart Citation

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Zhang,

Wang,

Luo

et al. 2023

Sensors

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Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

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Section: Discussionsupporting

confidence: 89%

Section: Spectral Fusion Strategiessupporting

confidence: 83%

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Zhang,

Wang,

Luo

et al. 2023

Sensors

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“…Soil texture characterization is usually carried on manually, being consequently time-consuming. Vis-NIR handheld spectroscopy is preferred as sensor to predict soil characteristics ( Bene et al, 2020 ; Benedet et al, 2020 ; Pham et al, 2021 ; Andrade et al, 2022 ; Naimi et al, 2022 ; Peng et al, 2022 ; Shrestha et al, 2022 ; Teixeira et al, 2022 ; Vohland et al, 2022 ).…”

Section: Portable Nir Spectrometersmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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“…Nevertheless, the primary challenge lies in the measurement uncertainty due to variable field conditions. Some authors have advised against underestimating metal concentrations using pXRF data and recommend additional soil sampling and geostatistical simulation (Horta et al, 2021;Qu et al, 2022), or other complementary spectroscopy analyses (Li et al, 2021;Shrestha et al, 2022) for more accurate analysis. To mitigate the effects of field conditions during pXRF measurements, several precautions have been reported in the literature, including slight compaction of the soil, removal of organic matter from the surface, and control of soil moisture J o u r n a l P r e -p r o o f (Sharma et al, 2014;Weindorf et al, 2012;Zhu et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Modeling soil moisture from in situ portable X-ray spectrometer measurements: A novel approach for correcting geochemical data across different environments and climatic conditions

Gloaguen,

Marinho Reis,

Philippe

et al. 2024

Applied Geochemistry

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Quantification of multiple soil trace elements by combining portable X-ray fluorescence and reflectance spectroscopy

Cited by 14 publications

References 62 publications

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Portable NIR spectroscopy: the route to green analytical chemistry

Modeling soil moisture from in situ portable X-ray spectrometer measurements: A novel approach for correcting geochemical data across different environments and climatic conditions

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