Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil | |
Wang, Liusan1,2; Wang, Rujing1,2 | |
刊名 | SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY |
2022-12-15 | |
卷号 | 283 |
关键词 | VIS-NIR spectroscopy Soil pH Lime concretion black soil Extreme learning machine Variable selection |
ISSN号 | 1386-1425 |
DOI | 10.1016/j.saa.2022.121707 |
通讯作者 | Wang, Liusan() |
英文摘要 | Variable selection is widely accepted as an important step in the quantitative analysis of visible and near-infrared (Vis-NIR) spectroscopy, as it tends to improve the model's robustness and predictive ability. In this study, a total of 140 lime concretion black soil samples were collected from two towns in Guoyang County, China. The Vis-NIR spectra measured in the laboratory were used to estimate soil pH by an extreme learning machine (ELM). First, the soil spectra were treated by the optimized continuous wavelet transform (CWT), and then four spectral feature selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; Monte Carlo uninformative variable elimination, MCUVE; genetic algorithm, GA) were applied with ELM in the CWT domain to determine the techniques with most predictions. For comparison, The PLS and SVM models were also developed. The coefficient of determination (R-2), root mean square error (RMSE), and residual pre-diction deviation (RPD) were used to evaluate the model performance. Based on the validation dataset, the performance of the ELM models was superior to that of the PLS and SVM models expect SPA and MCUVE. In the ELM models, the order of the prediction accuracy was GA-ELM (R-p(2) = 0.86; RMSEp = 0.1484; RPD = 2.64), CARS -ELM (R-p(2) = 0.84; RMSEp = 0.1565; RPD = 2.50), ELM (R-p(2) = 0.84; RMSEp = 0.1572; RPD = 2.49), SPA-ELM (R-p(2) = 0.84; RMSEp = 0.1589; RPD = 2.47) and MCUVE-ELM (R-p(2) = 0.83; RMSEp = 0.1599; RPD = 2.45). The proposed method of CARS-ELM had a relatively strong ability for spectral variable selection while retaining excellent prediction accuracy and short computing time (0.39 s). In addition, the variables selected by the four methods (CARS, SPA, MCUVE and GA) indicated the prediction mechanism for pH in lime concretion black soil may be the relation between pH and iron oxides and organic matter. In conclusion, CARS-ELM has great potential to accurately determine the pH in lime concretion black soil using Vis-NIR spectroscopy. |
资助项目 | Major Scientific and Technological Innovation Project of Shandong Province, China[2019JZZY010730] |
WOS关键词 | INFRARED REFLECTANCE SPECTROSCOPY ; ORGANIC-CARBON CONTENT ; GENETIC ALGORITHMS ; ONLINE MEASUREMENT ; PLS-REGRESSION ; PREDICTION ; ELIMINATION ; COMBINATION ; CALIBRATION ; ACCURACY |
WOS研究方向 | Spectroscopy |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:000848546000001 |
资助机构 | Major Scientific and Technological Innovation Project of Shandong Province, China |
内容类型 | 期刊论文 |
源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/131880] |
专题 | 中国科学院合肥物质科学研究院 |
通讯作者 | Wang, Liusan |
作者单位 | 1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Intelligent Agr Engn Lab Anhui Prov, Hefei 230031, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Liusan,Wang, Rujing. Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil[J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,2022,283. |
APA | Wang, Liusan,&Wang, Rujing.(2022).Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,283. |
MLA | Wang, Liusan,et al."Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 283(2022). |
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