Li Xiaolong, Huang Jing, Chen Rongqin, You Zhengkai, Peng Jiyu, Shi Qingcai, Li Gang, Liu Fei
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
J Hazard Mater. 2023 Apr 15;448:130885. doi: 10.1016/j.jhazmat.2023.130885. Epub 2023 Feb 2.
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.
快速准确地检测农业土壤中的铬（Cr）对于土壤污染评估具有重要意义。与传统化学方法相比，激光诱导击穿光谱法（LIBS）可作为一种快速且无需化学试剂的有害金属分析方法。然而，LIBS检测受到不确定性和基体效应的干扰。本研究提出了一种结合线性加权网络（LWNet）的平均策略来降低不确定性。提出了自适应加权归一化-LWNet（AWN-LWNet）框架以减少两种土壤类型中的基体效应。结果表明，LWNet优于传统机器学习方法，对于黄棕壤和赤红壤，其平均相对误差（ARE）分别为2.08%和3.03%。此外，即使在低光谱分辨率下，LWNet也能有效地挖掘Cr特征峰。与常用模型相比，AWN-LWNet是降低基体效应的最优模型（ARE = 4.12%）。此外，AWN-LWNet大大减少了模型输入的光谱变量数量（从22016个减少到72个）。通过从模型中提取Cr峰，可以直观地观察到Cr峰强度的差异，这为减少基体效应提供了光谱解释。这两种方法有潜力实现利用LIBS检测土壤中的有害金属。
