Ma Chundi, Xu Xinhang, Zhou Min, Hu Tao, Qi Chongchong
School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
School of Metallurgy and Environment, Central South University, Changsha 410083, China.
Toxics. 2024 May 11;12(5):357. doi: 10.3390/toxics12050357.
High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in soil. In this study, a deep neural network (DNN) approach was applied to the Land Use and Cover Area frame Survey (LUCAS) dataset to develop a hyperspectral soil Cr content prediction model with good generalizability and accuracy. The optimal DNN model was constructed by optimizing the spectral preprocessing methods and DNN hyperparameters, which achieved good predictive performance for Cr detection, with a correlation coefficient value of 0.79 on the testing set. Four important hyperspectral bands with strong Cr sensitivity (400-439, 1364-1422, 1862-1934, and 2158-2499 nm) were identified by permutation importance and local interpretable model-agnostic explanations. Soil iron oxide and clay mineral content were found to be important factors influencing soil Cr content. The findings of this study provide a feasible method for rapidly determining soil Cr content from hyperspectral data, which can be further refined and applied to large-scale Cr detection in the future.
土壤中高含量的铬（Cr）对人类和环境都构成了重大威胁。基于实验室的铬化学分析方法既耗时又昂贵；因此，迫切需要一种更高效的土壤铬检测方法。在本研究中，将深度神经网络（DNN）方法应用于土地利用与覆盖面积框架调查（LUCAS）数据集，以开发具有良好通用性和准确性的高光谱土壤铬含量预测模型。通过优化光谱预处理方法和DNN超参数构建了最优DNN模型，该模型在铬检测方面具有良好的预测性能，测试集上的相关系数值为0.79。通过排列重要性和局部可解释模型无关解释，确定了四个对铬敏感度较高的重要高光谱波段（400 - 439、1364 - 1422、1862 - 1934和2158 - 2499 nm）。发现土壤氧化铁和粘土矿物含量是影响土壤铬含量的重要因素。本研究结果为从高光谱数据快速测定土壤铬含量提供了一种可行方法，未来可进一步完善并应用于大规模铬检测。
