Rapid and Simultaneous Detection of Hazardous Heavy Metals Contamination in Agricultural Soil Using Infrared Reflectance Spectroscopy

1st South Aceh International Conference on Engineering and Technology

工业技术（总论）

Devianti, D.^1 ; Sufardi, S.^2 ; Zulfahrizal, Z.^1 ; Munawar, Agus Arip^1

Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh, Indonesia^1

Department of Soil Science, Syiah Kuala University, Banda Aceh, Indonesia^2

关键词: Coefficient correlations;    Hazardous contaminations;    Heavy metal contamination;    Infrared reflectance spectroscopy;    Multiplicative scatter correction;    Partial least square regression;    Principle component analysis;    Simultaneous detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/506/1/012008/pdf
DOI  :  10.1088/1757-899X/506/1/012008

学科分类：工业工程学

来源: IOP

【 摘 要 】

Soil function can be disrupted by highly accumulated contaminations in soils, due to several human activities such as building construction, mining, sewage disposal, transportation, and fertilizing. These activities may cause contaminations especially by heavy metals accumulated in soil and surely will affects growing plants on it. A real time, quick, robust and simultaneous method is therefore required to rapidly detect heavy metal contamination in agricultural soil. Infrared technology has been widely employed and applied in many field areas including in agriculture and soil science. The main aim of this research is to study the feasibility of infrared spectroscopy as a fast and robust method in detecting hazardous contaminations caused by accumulated heavy metals namely Zn and Pb in agricultural soil. Diffuse reflectance spectral data were acquired for a total of 8 soil samples from four different site locations in Aceh province. Spectra data, in wavelength range of 1000-2500 nm, were enhanced using multiplicative scatter correction (MSC). Hazardous detection was performed using principal component analysis (PCA) and accumulated heavy metals were predicted using partial least square regression (PLSR) method. The results showed that IR spectroscopy can clearly distinguish contaminated soil and un-contaminated ones, with total explained variance derived from PCA was 97%. Both accumulated heavy metals (Zn and Pb) can be predicted simultaneously with coefficient correlation (r) were 0.98 for Zn and 0.98 for Pb, whereas residual predictive deviation (RPD) indexed were 3.85 for Zn and 3.91 for Pb. It may conclude that infrared spectroscopy can be used as a fast, robust and accurate method in detecting and predicting heavy metal contaminations in agricultural soil.