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

Abstract

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.

Identification and Evolution of Soil Organic Carbon Density Caused by Coastal Rapid Siltation Based on Imaging Spectroscopy

“…Apart from mapping, carbon contents and properties in soil spectra are modelled using various calibration methods which include linear models, such as multiple linear regression, principal components regression, and partial least squares regression (PLSR) [30,[32][33][34][35]. In addition, nonlinear models, such as support vector machine (SVM), random forests (RF), and artificial neural networks (ANN), are often used to predict soil properties [32,[36][37][38]. Despite having the development of robust modelling, many scholars have explored biases in the accuracy of model predictions in SOC.…”

Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results

“…Hyperspectral imaging overcomes the problem of the local variability within the sample and the problem of the interpretation of a single measurement because it combines imagery and spectral behavior. Therefore, most hyperspectral applications and research result from this combination [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The human eye can easily interpret the image and identify different areas, interface regions, and buried objects.…”

Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

“…Visible-near infrared (Vis-NIR) spectroscopy has been a simple and efficient analytical technique which has been widely deployed for the detection of SOM content [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The high sensitivity of vis-NIR spectroscopy has been shown to be capable to detect trace minerals such as arsenic contamination in soil [ 24 ]. However, because of the strong moisture absorption peaks in the vicinity of the SOM features within the vis-NIR spectral range, the presence of moisture in the soil interferes the interpretation of SOM content significantly [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”