Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model.

Mei Guo,Lixin Zhang,Hongwei Duan,Rongguang Zhu,Guangqun Huang,Ruoyu Zhang

doi:10.3390/molecules26072069

Abstract

Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims first to apply laser-induced breakdown spectroscopy (LIBS) for the quantitative detection of Cr in biochar. Learning from the principles of traditional matrix effect correction methods, calibration samples were divided into 1–3 classifications by an unsupervised hierarchical clustering method based on the main elemental LIBS data in biochar. The prediction samples were then divided into diverse classifications of calibration samples by a supervised K-nearest neighbor (KNN) algorithm. By comparing the effects of multiple partial least squares regression (PLSR) models, the results show that larger numbered classifications have a lower averaged relative standard deviations of cross-validation (ARSDCV) value, signifying a better calibration performance. Therefore, the 3 classification regression model was employed in this study, which had a better prediction performance with a lower averaged relative standard deviations of prediction (ARSDP) value of 8.13%, in comparison with our previous research and related literature results. The LIBS technology combined with matrix effect classification regression model can weaken the influence of the complex matrix effect of biochar and achieve accurate quantification of contaminated metal Cr in biochar.

Highlights

Returning biochar to farmland has become a research hotspot in China, since it can improve the quality of cultivated land [1]
We investigated the feasibility of applying laser-induced breakdown spectroscopy (LIBS) technology for the quantitative analysis of heavy metal Cr in biochar
To reduce the influence of complex matrix effects, calibration samples of biochar were divided unsupervised into 1–3 classifications using the main elemental LIBS data by hierarchical classification method

Summary

Introduction

Returning biochar to farmland has become a research hotspot in China, since it can improve the quality of cultivated land [1]. Several rapid detection methods, such as biochemical sensors [3,4], test paper detection [5,6], the indicator biological method [7], enzyme-linked immunosorbent assay [8,9] and spectral analysis [10,11,12], have been widely used for metal detection in the fields of industrial analysis [13,14], biomedical engineering [15], food safety [16,17,18] and environmental ecological pollution assessment [19] This can reduce the limit of detection (LOD), but can improve the sensitivity and detection efficiency. It is urgent to develop a simpler spectral analysis instrument with little sample pretreatment to avoid the complex sample pretreatment of traditional analysis instruments [20,21,22]

Objectives

Results

Conclusion