Quantitative analysis of heavy metals in soil<i>via</i>hierarchical deep neural networks with X-ray fluorescence spectroscopy-Reference-Cited by-同舟云学术

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Quantitative analysis of heavy metals in soilviahierarchical deep neural networks with X-ray fluorescence spectroscopy

Published:2023 Issue:9 Volume:38 Page:1830-1840
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Yang Wanqi¹²^ORCID,Li Fusheng¹²^ORCID,Lyu Shubin¹²,Zhang Qinglun¹²,Zhao Yanchun¹²^ORCID

Affiliation:

1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China

2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, PR China

Abstract

Potentially toxic metals in soils are accurately predicted using hierarchical depth neural networks by X-ray fluorescence spectroscopy.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/JA/D3JA00120B

Reference47 articles.

1. World Health Organization , World Health Organization , 1996

2. Relevance, essentiality and toxicity of trace elements in human health

3. The biochemistry of environmental heavy metal uptake by plants: Implications for the food chain

4. A Review of Technologies for Characterization of Heavy Metal Contaminants

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An integrated CBLA-Net with fractional discrete wavelet transform and frequency-based CARS to predict heavy metal elements by XRF;Analytica Chimica Acta;2024-09

2. A deep spectral prediction network to quantitatively determine heavy metal elements in soil by X-ray fluorescence;Journal of Analytical Atomic Spectrometry;2024

3. Quantitative analysis of potentially toxic elements in soil by XRF based on efficient reinforcement learning and sparse partial least squares;Journal of Analytical Atomic Spectrometry;2024

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