Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil DOI Creative Commons
Yu Ding, Yufeng Wang, Jing Chen

et al.

Photonics, Journal Year: 2024, Volume and Issue: 11(2), P. 129 - 129

Published: Jan. 30, 2024

The quality and safety of edible vegetable oils are closely related to human life health, meaning it is great significance explore the rapid detection methods pesticide residues in oils. This study explored applicability potential substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion soybean First, we impact laser energy, delay time, average oil film thickness on spectral signals identify best experimental parameters. Afterward, analyzed samples using these optimized conditions developed a full-spectrum extreme learning machine (ELM) model. model achieved prediction correlation coefficient (RP2) 0.8417, root mean square error (RMSEP) 167.2986, absolute percentage (MAPEP) 26.46%. In order enhance performance model, modeling method Boruta algorithm combined with ELM was proposed. employed feature variables that exhibit strong content. These selected were utilized as inputs RP2, RMSEP, MAPEP Boruta-ELM being 0.9631, 71.4423, 10.06%, respectively. Then, genetic (GA) used optimize parameters GA-Boruta-ELM 0.9962, 11.005, 1.66%, findings demonstrate exhibits excellent capability effectively predicts contents samples. It will be valuable LIBS quantitative analysis

Language: Английский

Systematic assessments of ecological and health risks of soil pesticide residues DOI
Tao Tang,

Chaotang Lei,

Lu Lv

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126348 - 126348

Published: April 1, 2025

Language: Английский

Citations

0
The Diverse Mycorrizal Morphology of Rhododendron dauricum, the Fungal Communities Structure and Dynamics from the Mycorrhizosphere DOI Creative Commons

Jin Liu,

Yang Xu,

Yan-Ji Si

et al.

Journal of Fungi, Journal Year: 2024, Volume and Issue: 10(1), P. 65 - 65

Published: Jan. 14, 2024

It is generally believed that mycorrhiza a microecosystem composed of mycorrhizal fungi, host plants and other microscopic organisms. The Rhododendron dauricum more complex the diverse morphology our investigated results displays both typical ericoid characteristics ectomycorrhizal traits. ectendoomycorrhiza, where mycelial invade from outside into root cells, have also been observed. In order to further clarify fungi members fungal communities R. mycorrhiza, explore effects vegetation soil biological factors on their community structure, we selected two woodlands in northeast China as samples—one mixed forest Quercus mongolica, dauricum, Q. Pinus densiflor. sampling time was during local growing season, June September. High-throughput sequencing yielded total 3020 amplicon sequence variants (ASVs), which were based internal transcribed spacer ribosomal RNA (ITS rRNA) via Illumina NovaSeq platform. different habitats there are differences diversity obtained niches, specifically structure forests, found, exhibits greater stability, with relatively minor changes over time. Soil identified primary source within niche, abundance niches significantly influenced by pH, organic matter, available nitrogen. relationship between simultaneously found be intricate, while genus Hydnellum emerges central among niches. However, currently substantial gap foundational research this genus, including fact have, compared present soil, proven sensitive moisture.

Language: Английский

Citations

3
Soil Formation, Soil Health and Soil Biodiversity DOI
O.A. Adewara,

T.C. Adebayo-Olajide,

J. S. Ayedun

et al.

Earth and environmental sciences library, Journal Year: 2024, Volume and Issue: unknown, P. 95 - 121

Published: Jan. 1, 2024

Language: Английский

Citations

3
A comparative evaluation of biochar and Paenarthrobacter sp. AT5 for reducing atrazine risks to soybeans and bacterial communities in black soil DOI
Jean Damascene Harindintwali, Chao He, Xin Wen

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 252, P. 119055 - 119055

Published: May 6, 2024

Language: Английский

Citations

3
Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil DOI Creative Commons
Yu Ding, Yufeng Wang, Jing Chen

et al.

Photonics, Journal Year: 2024, Volume and Issue: 11(2), P. 129 - 129

Published: Jan. 30, 2024

The quality and safety of edible vegetable oils are closely related to human life health, meaning it is great significance explore the rapid detection methods pesticide residues in oils. This study explored applicability potential substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion soybean First, we impact laser energy, delay time, average oil film thickness on spectral signals identify best experimental parameters. Afterward, analyzed samples using these optimized conditions developed a full-spectrum extreme learning machine (ELM) model. model achieved prediction correlation coefficient (RP2) 0.8417, root mean square error (RMSEP) 167.2986, absolute percentage (MAPEP) 26.46%. In order enhance performance model, modeling method Boruta algorithm combined with ELM was proposed. employed feature variables that exhibit strong content. These selected were utilized as inputs RP2, RMSEP, MAPEP Boruta-ELM being 0.9631, 71.4423, 10.06%, respectively. Then, genetic (GA) used optimize parameters GA-Boruta-ELM 0.9962, 11.005, 1.66%, findings demonstrate exhibits excellent capability effectively predicts contents samples. It will be valuable LIBS quantitative analysis

Language: Английский

Citations

2