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

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 (AWNLWNet) 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

Automated summary

Rapid and accurate detection of agricultural soil chromium is crucial for soil pollution assessment. Laser-induced breakdown spectroscopy (LIBS) is a rapid and chemical-free method for hazardous metal analysis, but its detection is interfered by uncertainty and matrix effect. In this study, a strategy combining linear weighted network (LWNet) was proposed to reduce uncertainty, and the 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 an average relative error of 2.08% and 3.03% for yellow brown soil and lateritic red soil, respectively. AWN-LWNet was the optimal model to reduce matrix effect (ARE=4.12%). Besides, AWN-LWNet greatly reduced the number...