Abstract
Using visible and near-infrared (Vis-NIR) spectroscopy combined with partial least squares (PLS) regression, the rapid reagent-free analysis model for chromium (Cr) content in tideland reclamation soil in the Pearl River Delta, China was established. Based on Savitzky-Golay (SG) smoothing and PLS regression, a multi-parameters optimization platform (SG-PLS) covering 264 modes was constructed to select the appropriately spectral preprocessing mode. The optimal SG-PLS model was determined according to the prediction effect. The selected optimal parameters d, p, m and LV were 2, 6, 23 and 8, respectively. Using the validation samples that were not involved in modeling, the root mean square error (SEPV), relative root mean square error (R-SEPV) and correlation coefficients (RP, V) of prediction were 11.66 mg·kg-1, 10.7% and 0.722, respectively. The results indicated that the feasibility of using Vis-NIR spectroscopy combined with SG-PLS method to analyze soil Cr content. The constructed multi-parameters optimization platform with SG-PLS is expected to be applied to a wider field of analysis. The rapid detection method has important application values to large-scale agricultural production.
Highlights
Using visible and near-infrared (Vis-Near infrared (NIR)) spectroscopy combined with partial least squares (PLS) regression, the rapid reagent-free analysis model for chromium (Cr) content in tideland reclamation soil in the Pearl River Delta, China was established
The results indicated that the feasibility of using Vis-NIR spectroscopy combined with SG-PLS method to analyze soil Cr content
The literature (Chen et al, 2015) used Vis-NIR spectroscopy combined with partial least squares (PLS) and back propagation neural network (BPNN) to estimate soil cadmium (Cd) concentration in irrigated areas in northern China
Summary
Soil heavy metal pollution refers to the phenomenon of excessive heavy metal. There are many causes of heavy metal pollution in soil, such as industrial waste discharge, mining and so on. The detection of soil heavy metal is an important basic work. Due to the extremely uneven temporal and spatial distribution of soil components, intensive dynamic sampling is required, and a large sample amount of testing is required to obtain objective evaluation results
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