Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS matrix effect reduction

Xiaolong Li; Jing Huang; Rongqin Chen; Zhengkai You; Jiyu Peng; Qingcai Shi; Gang Li; Fei Liu

doi:10.1016/j.jhazmat.2023.130885

Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS matrix effect reduction

J Hazard Mater. 2023 Apr 15:448:130885. doi: 10.1016/j.jhazmat.2023.130885. Epub 2023 Feb 2.

Authors

Xiaolong Li¹, Jing Huang¹, Rongqin Chen¹, Zhengkai You¹, Jiyu Peng², Qingcai Shi³, Gang Li⁴, Fei Liu⁵

Affiliations

¹ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
² College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
³ QIN Soil Testing Laboratory (Shandong) Co., Ltd, Shidanli Road, Linshu 276700, China.
⁴ CAS Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Jiaxing Key Lab of Soil Health, Yangtze Delta Region Healthy Agriculture Institute, Jiaxing 314503, China.
⁵ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. Electronic address: fliu@zju.edu.cn.

PMID: 36738619
DOI: 10.1016/j.jhazmat.2023.130885

Abstract

Rapid and accurate detection of agricultural soil chromium (Cr) is of great significance for soil pollution assessment. Laser-induced breakdown spectroscopy (LIBS) could serve as a rapid and chemical-free method for hazardous metal analysis compared with conventional chemical methods. However, the detection of LIBS is interfered by uncertainty and matrix effect. In this study, an average strategy combined with linear weighted network (LWNet) was proposed to reduce the uncertainty. Adaptive weighted normalization-LWNet (AWN-LWNet) framework was proposed to reduce the matrix effect in two soil types. The results indicated that LWNet outperformed traditional machine learning and achieved the average relative error (ARE) of 2.08 % and 3.03 % for yellow brown soil and lateritic red soil, respectively. Moreover, LWNet could effectively mine Cr feature peaks even under the low spectral resolution. AWN-LWNet was the optimal model compared with commonly used models to reduce matrix effect (ARE=4.12 %). Besides, AWN-LWNet greatly reduced the number (from 22016 to 72) of spectral variables for model input. By extracting Cr peaks from models, the difference of Cr peaks intensity could be intuitively observed, which served as spectral interpretation for matrix effect reduction. The two methods have the potential to realize the detection of hazardous metals in soil by LIBS.

Keywords: Laser-induced breakdown spectroscopy; Matrix effect; Rapid detection; Soil hazardous metal.