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

Abstract

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.

Keywords:

• Infrared (IR) spectroscopy
• near-infrared (NIR) spectroscopy
• partial least squares (PLS)
• phosphogypsum
• soil
• support vector machine (SVM)

Additional information

Funding

This work was supported by the Guizhou Provincial Science and Technology Project (grant numbers ZK[2022]063, ZK[2022]126, and [2020]4Y133), the Science and Technology Project of China National Tobacco Corporation Bijie Company (grant number 2022520500240187), and the National Key R&D Program of China (grant number 2018YFC1903500).