Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model
Abstract
:1. Introduction
2. Results and Discussion
2.1. Matrix Elemental Analysis
2.2. Spectral Classification of Matrix Effect
2.3. Classification Regression Model
3. Experimental Design and Methods
3.1. LIBS Device
3.2. Sample Preparation
3.3. Matrix Effect Classification Regression Modeling and Evaluation Criteria
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
References
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Main Matrix | 2 classifications | 3 Classifications |
---|---|---|
C+O (76.85%) | C1: 1–25 (calibration), 47–53 (prediction) C2: 26–46 (calibration), 54–60(prediction) | c1: 1–25 (calibration), 47–53 (prediction) c2: 26–39 (calibration), 54–58 (prediction) c3: 40–46 (calibration), 59–60 (prediction) |
Classifications | Classification | RMSECV (mg/kg) | RSDCV (%) | ARSDCV (%) |
---|---|---|---|---|
1 | None | 2.38 | 18.53% | 18.53% |
2 | C1 | 1.99 | 12.70% | 18.11% |
C2 | 2.23 | 23.51% | ||
3 | c1 | 1.99 | 12.70% | 12.32% |
c2 | 1.38 | 11.02% | ||
c3 | 0.45 | 13.24% |
Particle | Element | Range (mg/kg) | RSDP/ARSDP (%) | Remarks | Ref. |
---|---|---|---|---|---|
Biochar | Cr | 2.92–25.38 | 8.13% | MEC-PLS 1 | In this work |
Soil | Cr | 48–410 | 23.019 | MIPW-PLS 2 | Fu et al. 2017 [31] |
Soil | Cr | 48–410 | 17.673 | FSC-MIPW-PLS 3 | Duan et al. 2018 [32] |
Soil | Cr | 18.29–164.06 | 11.460 | Lasso 4 | Wang et al. 2018 [33] |
Biochar | Cr | 5.05–19.15 | 17.41% | PLS | Duan et al. 2019 [34] |
Derivation (#1~#15) | Content | Derivation (#16~#30) | Content | Derivation (#31~#45) | Content | Derivation (#46~#60) | Content |
---|---|---|---|---|---|---|---|
rice husk | 7.37 | corn stalk | 18.21 | rice straw | 10.67 | rice husk | 4.5 |
rice husk | 7.47 | corn stalk | 18.63 | rice straw | 11.47 | rice husk | 7.42 |
rice husk | 7.83 | corn stalk | 18.88 | rice husk | 11.73 | rice husk | 8.15 |
rice husk | 8.11 | corn stalk | 18.9 | rice straw | 12.81 | rice husk | 11.51 |
rice husk | 8.22 | corn stalk | 18.98 | rice husk | 13.75 | corn stalk | 17.59 |
rice husk | 8.53 | corn stalk | 19.15 | rice husk | 15.15 | corn stalk | 18.55 |
rice husk | 10.59 | rice husk | 19.56 | rice straw | 16.47 | corn stalk | 18.91 |
rice husk | 11.54 | corn stalk | 26.15 | corn stalk | 17.08 | corn stalk | 25.39 |
rice husk | 12.11 | corn stalk | 26.34 | corn stalk | 20.15 | rice straw | 7.99 |
rice husk | 12.52 | corn stalk | 28.51 | rice straw | 2.79 | rice straw | 10.36 |
corn stalk | 15.27 | rice straw | 7.9 | rice straw | 2.9 | rice straw | 11.5 |
corn stalk | 15.61 | rice straw | 8.37 | rice straw | 3.04 | rice straw | 13.81 |
rice husk | 17.28 | rice straw | 9.32 | rice straw | 3.28 | corn stalk | 18.22 |
corn stalk | 17.94 | rice straw | 9.9 | rice straw | 3.3 | rice straw | 2.92 |
corn stalk | 18.03 | rice straw | 10.6 | rice straw | 3.98 | rice straw | 3.32 |
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Guo, M.; Zhu, R.; Zhang, L.; Zhang, R.; Huang, G.; Duan, H. Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model. Molecules 2021, 26, 2069. https://doi.org/10.3390/molecules26072069
Guo M, Zhu R, Zhang L, Zhang R, Huang G, Duan H. Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model. Molecules. 2021; 26(7):2069. https://doi.org/10.3390/molecules26072069
Chicago/Turabian StyleGuo, Mei, Rongguang Zhu, Lixin Zhang, Ruoyu Zhang, Guangqun Huang, and Hongwei Duan. 2021. "Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model" Molecules 26, no. 7: 2069. https://doi.org/10.3390/molecules26072069