Cargando…
Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model
Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims fir...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038433/ https://www.ncbi.nlm.nih.gov/pubmed/33916837 http://dx.doi.org/10.3390/molecules26072069 |
_version_ | 1783677374237245440 |
---|---|
author | Guo, Mei Zhu, Rongguang Zhang, Lixin Zhang, Ruoyu Huang, Guangqun Duan, Hongwei |
author_facet | Guo, Mei Zhu, Rongguang Zhang, Lixin Zhang, Ruoyu Huang, Guangqun Duan, Hongwei |
author_sort | Guo, Mei |
collection | PubMed |
description | Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims first to apply laser-induced breakdown spectroscopy (LIBS) for the quantitative detection of Cr in biochar. Learning from the principles of traditional matrix effect correction methods, calibration samples were divided into 1–3 classifications by an unsupervised hierarchical clustering method based on the main elemental LIBS data in biochar. The prediction samples were then divided into diverse classifications of calibration samples by a supervised K-nearest neighbor (KNN) algorithm. By comparing the effects of multiple partial least squares regression (PLSR) models, the results show that larger numbered classifications have a lower averaged relative standard deviations of cross-validation (ARSDCV) value, signifying a better calibration performance. Therefore, the 3 classification regression model was employed in this study, which had a better prediction performance with a lower averaged relative standard deviations of prediction (ARSDP) value of 8.13%, in comparison with our previous research and related literature results. The LIBS technology combined with matrix effect classification regression model can weaken the influence of the complex matrix effect of biochar and achieve accurate quantification of contaminated metal Cr in biochar. |
format | Online Article Text |
id | pubmed-8038433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80384332021-04-12 Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model Guo, Mei Zhu, Rongguang Zhang, Lixin Zhang, Ruoyu Huang, Guangqun Duan, Hongwei Molecules Article Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims first to apply laser-induced breakdown spectroscopy (LIBS) for the quantitative detection of Cr in biochar. Learning from the principles of traditional matrix effect correction methods, calibration samples were divided into 1–3 classifications by an unsupervised hierarchical clustering method based on the main elemental LIBS data in biochar. The prediction samples were then divided into diverse classifications of calibration samples by a supervised K-nearest neighbor (KNN) algorithm. By comparing the effects of multiple partial least squares regression (PLSR) models, the results show that larger numbered classifications have a lower averaged relative standard deviations of cross-validation (ARSDCV) value, signifying a better calibration performance. Therefore, the 3 classification regression model was employed in this study, which had a better prediction performance with a lower averaged relative standard deviations of prediction (ARSDP) value of 8.13%, in comparison with our previous research and related literature results. The LIBS technology combined with matrix effect classification regression model can weaken the influence of the complex matrix effect of biochar and achieve accurate quantification of contaminated metal Cr in biochar. MDPI 2021-04-03 /pmc/articles/PMC8038433/ /pubmed/33916837 http://dx.doi.org/10.3390/molecules26072069 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guo, Mei Zhu, Rongguang Zhang, Lixin Zhang, Ruoyu Huang, Guangqun Duan, Hongwei Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model |
title | Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model |
title_full | Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model |
title_fullStr | Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model |
title_full_unstemmed | Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model |
title_short | Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model |
title_sort | quantitative detection of chromium pollution in biochar based on matrix effect classification regression model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038433/ https://www.ncbi.nlm.nih.gov/pubmed/33916837 http://dx.doi.org/10.3390/molecules26072069 |
work_keys_str_mv | AT guomei quantitativedetectionofchromiumpollutioninbiocharbasedonmatrixeffectclassificationregressionmodel AT zhurongguang quantitativedetectionofchromiumpollutioninbiocharbasedonmatrixeffectclassificationregressionmodel AT zhanglixin quantitativedetectionofchromiumpollutioninbiocharbasedonmatrixeffectclassificationregressionmodel AT zhangruoyu quantitativedetectionofchromiumpollutioninbiocharbasedonmatrixeffectclassificationregressionmodel AT huangguangqun quantitativedetectionofchromiumpollutioninbiocharbasedonmatrixeffectclassificationregressionmodel AT duanhongwei quantitativedetectionofchromiumpollutioninbiocharbasedonmatrixeffectclassificationregressionmodel |