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Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil

文献类型：期刊论文

作者Wang, Liusan1,2; Wang, Rujing1,2
刊名SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
出版日期2022-12-15
卷号283
ISSN号1386-1425
关键词VIS-NIR spectroscopy Soil pH Lime concretion black soil Extreme learning machine Variable selection
DOI10.1016/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.
WOS关键词INFRARED REFLECTANCE SPECTROSCOPY ; ORGANIC-CARBON CONTENT ; GENETIC ALGORITHMS ; ONLINE MEASUREMENT ; PLS-REGRESSION ; PREDICTION ; ELIMINATION ; COMBINATION ; CALIBRATION ; ACCURACY
资助项目Major Scientific and Technological Innovation Project of Shandong Province, China[2019JZZY010730]
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
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.Intelligent Agr Engn Lab Anhui Prov, Hefei 230031, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, 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).

入库方式： OAI收割

来源：合肥物质科学研究院

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