Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS matrix effect reduction

Li, Xiaolong; Huang, Jing; Chen, Rongqin; You, Zhengkai; Peng, Jiyu; Shi, Qingcai; Li, Gang; Liu, Fei

doi:10.1016/j.jhazmat.2023.130885

Cited by 12 publications

(4 citation statements)

References 43 publications

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“…The second featured 165 co-linear DP-LIBS and a particle-swarm-optimised least-squares support vector machine. The performance of a linear-weighted network was improved 166 for the determination of Cr by addition of adaptive weighted normalisation. The application of a novel ion-enrichment chip containing an anion exchange resin (for Cr VI ) and an active carbon fibre (for total Cr) made 167 speciation analysis possible.…”

Section: Analysis Of Soils Plants and Related Materialsmentioning

confidence: 99%

Atomic spectrometry update – a review of advances in environmental analysis

Bacon,

Butler,

Cairns

et al. 2024

J. Anal. At. Spectrom.

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Section: Analysis Of Soils Plants and Related Materialsmentioning

confidence: 99%

Atomic spectrometry update – a review of advances in environmental analysis

Bacon,

Butler,

Cairns

et al. 2024

J. Anal. At. Spectrom.

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“…This improvement is essential when According to the principle of LIBS, a narrow line dominates corresponding to the element, and the line intensity is proportional to the atomic concentration [48]. The intensity of the element emission line goes up with the concentration when the interference is small, showing a linear relationship [49]. To verify the difference in the models' performances between a single brand and various brands, we analyzed the emission lines of Cd based on the calibration curve model.…”

Section: Spectral Profilementioning

confidence: 99%

Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Chen,

Li,

et al. 2024

Foods

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Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater–spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater–spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater–spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

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“…By collecting and analysing the emission spectra of atoms and ions in the plasma, qualitative or quantitative information about the sample can be quickly determined [3]. However, matrix effects and signal uncertainty can severely degrade the quality of LIBS signals, resulting in less satisfactory performance in LIBS quantification [4,5]. Some of the important variables, affected by noise interference, may exhibit weakened correlations with coal properties, while certain unrelated variables, due to coincidental factors when combined with noise, may exhibit statistically significant correlations with coal properties.…”

Section: Introductionmentioning

confidence: 99%

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

LYU 吕,

SONG 宋,

HOU 侯

et al. 2024

Plasma Sci. Technol.

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Nowadays, laser-induced breakdown spectroscopy (LIBS) has become a widely used atomic spectroscopic technique for rapid coal analysis. While the vast spectral information in LIBS contains signal uncertainty, which can impact its quantification performance. In this work, we proposed a hybrid variable selection method to improve the performance of LIBS quantification. Important variables are first identified using Pearson’s correlation coefficient (PCC), mutual information (MI), least absolute shrinkage and selection operator (LASSO) and random forest (RF), and then filtered and combined with empirical variables related to fingerprint elements of coal ash content. Subsequently, these variables are fed into partial least squares regression (PLSR). Additionally, in some models, certain variables unrelated to ash content were removed manually to study the variable deselection’s impact on model performance. The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method. It is significantly better than the variable selection only based on empirical knowledge and in most cases outperforms the baseline method. The results showed that on all three datasets, hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest RMSEP values of 1.605, 3.478 and 1.647, respectively, which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables, which are 1.959, 3.718 and 2.181, respectively. The EMP-LASSO-PLSR model with 20 selected variables exhibited a significant improving performance after variable deselection, with RMSEP values dropping from 1.635, 3.962, 1.647 to 1.483, 3.086, 1.567, respectively. Such results demonstrate that using empirical knowledge as a support to data-driven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

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Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS matrix effect reduction

Cited by 12 publications

References 43 publications

Atomic spectrometry update – a review of advances in environmental analysis

Atomic spectrometry update – a review of advances in environmental analysis

Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

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