Rapid Determination of Phosphogypsum in Soil Based by Infrared (IR) and Near-Infrared (NIR) Spectroscopy with Multivariate Calibration

The application and accumulation of phosphogypsum (PG) may cause soil pollution, so it is of significance to establish a rapid method for its determination in soil. In this study, the feasibility of quantifying PG in soil by multivariate calibration combined with portable near-infrared spectroscopy (NIR) and infrared spectroscopy (IR) was investigated. In order to obtain better accuracy, standard normal variable (SNV) and Savitzky-Golay smoothing were employed as the pretreatment methods for IR and NIR, respectively. The competitive adaptive reweighted sampling (CARS) algorithm was used for variable optimization of these models. The results show that the predictive determination coefficient and root mean square error of prediction (RMSEP) of IR and NIR partial least squares (PLS) models were 0.9933 and 1.88% and 0.8830 and 6.55%. The limits of detection (LOD) for the models were 4.0006% and 14.225%. The reproducibility of the models is satisfactory with good accuracy and precision. In addition, extreme learning machine (ELM) and support vector machine (SVM) algorithms were also used to analyze the data, resulting in similar outcomes to those obtained by PLS. The results of a dual t test demonstrated that there is no significant difference between these methods and the standard procedure (GB/T 23456-2018) at the 95% confidence level. However, the reported protocols have the advantages of on-site analysis, speed, and convenience for the determination of phosphogypsum in soil.

Automated summary

This study investigates the feasibility of using portable near-infrared spectroscopy (NIR) and infrared spectroscopy (IR) combined with multivariate calibration to determine phosphogypsum (PG) in soil. The predictive determination coefficients and root mean square error of prediction (RMSEP) for the IR and NIR partial least squares (PLS) models are 0.9933 and 1.88% and 0.8830 and 6.55%, respectively. The limits of detection (LOD) for the models are 4.0006% and 14.225%. The results obtained by extreme learning machine (ELM) and support vector machine (SVM) algorithms are similar to those obtained by PLS. The methods reported in this study have the advantages of on-site analysis, speed, and convenience for the determination of phosphogypsum in soil.