A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil

Visible and near-infrared reflectance (VNIR) spectroscopy is considered to be a potential and efficient means for monitoring soil arsenic (As) contamination. While current studies mainly focus on the evaluation of models' performance when training and verification samples are collected from the same region, whether the model developed at a specific region can be transferred to other regions is still unclear. To answer this question, this study collected a total of 247 samples for training and verification from regions with different geographical conditions, which are Yuanping and Baoding in northern China, Chenzhou and Hengyang in southern China. Afterward, we proposed a transfer component analysis (TCA) based spectroscopic diagnosis model, which aims at adapting a model learned from one region to other regions. This model was compared with the traditional modeling method in terms of the prediction accuracy by four experiments. The results show that: (1) The traditional modeling method trained by specific regional samples has no transfer capability to different regions, since the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) were 0.02 and 0.65 for the first pair of study areas, 0.01 and 1.01 for the second pair of study areas; (2) A transfer model with favorable predictability can be constructed with the aid of TCA spectral transformation and a small amount off-site samples (R2 and RPD were improved to 0.68 and 1.54 for the first pair of study areas, 0.64 and 1.66 for the second pair of study areas). Results suggest that it is promising to develop potential implementations of transferable spectroscopic diagnosis models for estimating soil As concentrations in large area with lower cost. [Display omitted] •A transferable model was proposed to predict arsenic concentration in soil.•The transferable model can be implemented to large scale area at lower cost.•Transfer component analysis is effective for soil spectral transform.