Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine

Abstract

Soil contamination by heavy metals is of great concern due to the increasing intensity of anthropogenic activities and requires a reliable approach to rapidly diagnose heavy metal pollution in soils. In this study conducted in northern Shandong Province, China, a total of 110 soil samples were collected to determine the zinc concentration and their visible and near-infrared reflectance spectroscopy was applied to diagnose zinc contaminated soils. Pearson’s correlation analysis and competitive adaptive reweighted sampling algorithm were compared to select active bands as independent variables. Partial least squares regression and particle swarm optimization support vector machine method were applied to estimate zinc concentration and a leave-one-out cross-validation was used to validate data sets. Results show that the obvious accumulations of zinc were in topsoil at the study area. Partial least squares regression estimates of zinc concentrations were improved with competitive adaptive reweighted sampling algorithm. The optimized support vector machine model, based on the first derivative of spectra, achieved the best accuracy for zinc concentration estimation with ratio of performance to deviation of 2.74. The method of active band extracting by competitive adaptive reweighted sampling algorithm was responsible for the improved model performance because competitive adaptive reweighted sampling algorithms determined active bands which can be useful for interpreting complex relations with soil compounds, such as soil organic matter, iron oxide, and clay minerals. In addition, the climate in specific research areas plays an important role in active band selection as a result of soil constituent changes. This study indicated that a combination model of competitive adaptive reweighted sampling algorithm and optimized support vector machine (R² = 0.88; RMSE = 6.74, RPD = 2.74) can be recommended for estimating zinc concentrations in polluted soils and can provide improved understanding of the estimation mechanism and an alternative approach for diagnosing soil contamination.

Keywords:

• An improved model
• diagnosis
• soil
• visible and near-infrared spectroscopy
• zinc contamination

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This research is supported by National Natural Science Foundation of China [Grant No.: 41371395].