Abstract
Using near-infrared (NIR) spectroscopy combined with an optimal method for Savitzky-Golay (SG) smoothing and partial least squares (PLS) regression, a rapid analysis method was established for copper content in the beach reclamation soil samples from Pearl River Delta in China. A framework with calibration, prediction and validation was established by considering randomness and stability. The parameters were optimized according to the comprehensive index (SEP+) to produce modeling stability. The validation results show that, based on the SG-PLS model in long-NIR region (1100 - 2498 nm) with first-order derivative, fifth degree polynomial, seven smoothing points and six PLS factors, the corresponding root mean square error (SEP), correlation coefficient of prediction (RP) and average relative error (ARE) were 0.31 mg·kg-1, 0.924 and 4.5%, respectively. The result indicates high prediction accuracy. The relevant parameter selection can also provide a reference for designing small and dedicated spectrometer.
Highlights
Heavy metal pollution of soil refers to the excessive content of trace heavy metal elements in soil caused by human activities and deposition
Using near-infrared (NIR) spectroscopy combined with an optimal method for Savitzky-Golay (SG) smoothing and partial least squares (PLS) regression, a rapid analysis method was established for copper content in the beach reclamation soil samples from Pearl River Delta in China
According to the PRC National Standard [20] and Environmental Protection Industrial Standards of China [21], copper content in soil samples was measured by using flame atomic absorption spectrometry, which was used as the reference value for the calibration and validation of spectroscopic analysis
Summary
Heavy metal pollution of soil refers to the excessive content of trace heavy metal elements in soil caused by human activities and deposition. NIR spectrum has weak absorption intensity and can measure most types of samples directly without preprocessing and reagents. It has distinctive advantages in fast, real-time, online and in situ analysis and has been applied successfully in principal component (organic matters and total nitrogen) analysis of soil [1] [2] [3] [4] [5], water quality analysis [6], crop and food analysis [7] [8] [9] [10], petrochemical engineering [11] [12], biomedicines [13] [14] [15], etc
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