Affiliation:
1. Key Laboratory of Agricultural Internet of Things
2. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service
Abstract
Cadmium (Cd) can migrate in the soil and is readily absorbed by crops. High Cd accumulated in grains poses a huge threat to human health by inhibiting the function of the kidney system. Thus, it is crucial to reveal the content of soil Cd in vertical-depth series using a fast, real-time, and reliable method. For this purpose, laser-induced breakdown spectroscopy (LIBS) combined with multivariate chemometrics was developed to analyze Cd content in the soil with vertical-depth series. Soil samples spiked with different levels of Cd were prepared, and LIBS spectra were obtained by single-pulse LIBS (SP-LIBS) and collinear double-pulse LIBS (CDP-LIBS) with wavelengths of 532 nm and 1064 nm. With appropriate parameters, CDP-LIBS showed better performance in detecting Cd than SP-LIBS. Partial least squares regression (PLSR), genetic algorithm (GA)-optimized back propagation artificial neural network (BP-ANN), and particle swarm optimization (PSO)-optimized least squares-support vector machine (LS-SVM) were tested for quantitative analysis of the spectra after median absolute deviation (MAD), multiple scattering correction (MSC), wavelet transform (WT), spectral averaging, and normalization. PSO-optimized LS-SVM yielded an ideal result, with a coefficient of determination (R2, 0.999) and root mean square error (RMSE, 0.359 mg/Kg) in the prediction dataset. Finally, CDP-LIBS coupled with PSO-optimized LS-SVM was employed to analyze soil Cd content in vertical-depth series to reveal the migration pattern of Cd. Our results indicated that soil Cd had a significant positive relationship with the inverse of soil depth. However, Cd was mainly concentrated in 0-20 cm and rarely leached below 45 cm in the soil. This study suggests that LIBS and its enhancement techniques provide a reliable method for revealing the content of soil Cd in vertical-depth series.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province
Subject
Atomic and Molecular Physics, and Optics
Cited by
7 articles.
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