Author(s)

Guangwei Li¹, Jing Zhang¹, Tao Pan¹, Jing Yu², Lijun Yao^1*

¹Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
²South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, China.

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 (R_P) 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.

Soil, Copper, Near-Infrared Spectroscopy, Savitzky-Golay Smoothing, Partial Least Squares Regression

Li, G. , Zhang, J. , Pan, T. , Yu, J. and Yao, L. (2018) Rapid Analysis of Soil Copper Content for the Pearl River Delta Based on Near-Infrared Spectroscopy Combined with SG-PLS. American Journal of Analytical Chemistry, 9, 622-632. doi: 10.4236/ajac.2018.912045.