Next Article in Journal
Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
Next Article in Special Issue
Raman Spectral Analysis for Quality Determination of Grignard Reagent
Previous Article in Journal
An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection
Previous Article in Special Issue
Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste
Article

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

1
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
2
Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China
3
Hubei Provincial Institute of Land and Resources, Wuhan 430070, China
4
School of Printing and Packaging, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2941; https://doi.org/10.3390/app10082941
Received: 17 March 2020 / Revised: 22 April 2020 / Accepted: 22 April 2020 / Published: 24 April 2020
(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)
Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content. View Full-Text
Keywords: hyperspectral imaging; soil arsenic; extremely randomized trees regression hyperspectral imaging; soil arsenic; extremely randomized trees regression
Show Figures

Figure 1

MDPI and ACS Style

Wei, L.; Zhang, Y.; Yuan, Z.; Wang, Z.; Yin, F.; Cao, L. Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil. Appl. Sci. 2020, 10, 2941. https://doi.org/10.3390/app10082941

AMA Style

Wei L, Zhang Y, Yuan Z, Wang Z, Yin F, Cao L. Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil. Applied Sciences. 2020; 10(8):2941. https://doi.org/10.3390/app10082941

Chicago/Turabian Style

Wei, Lifei, Yangxi Zhang, Ziran Yuan, Zhengxiang Wang, Feng Yin, and Liqin Cao. 2020. "Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil" Applied Sciences 10, no. 8: 2941. https://doi.org/10.3390/app10082941

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop