数字农科院2.0

Fusion optimization: enhancing the efficacy of soil available heavy metal content prediction models leveraging novel near-infrared and mid-infrared spectroscopy technology

文献类型： 外文期刊

作者： Guo, Hairong;Guo, Mingdian;Liu, Yujia

作者机构：

关键词： Near-infrared;Mid-infrared;Fusion model;Heavy metal;Soil;Machine learning

期刊名称： MEASUREMENT

ISSN： 0263-2241

年卷期： 2025 年 261 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： When assessing soil heavy metal (HM) pollution, traditional methods often prioritize the total content of metals while overlooking the bioavailable fraction, which directly influences ecological cycles. This approach may omit critical information required to evaluate the potential hazards of HMs to organisms. Innovatively, this study focused on the precise characterization of bioavailable HMs in soil. Prediction models were constructed based on near-infrared (NIR) spectroscopy and mid-infrared spectroscopy (MIR), and the prediction performance of bioavailable HMs across different spectral bands was analyzed. Building on this, an NIR-MIR fusion model was further developed, which was tailored to the unique spectral response intervals of specific HMs. The results indicated that NIR spectroscopy exhibited significant advantages in the accurate quantification of key bioavailable HMs (i.e., available Fe, Mn, Cu, and Zn), with prediction errors ranging from 5.67% to 11.31%. By expanding the dimension of effective information, the fusion strategy notably enhanced the efficiency of spectral information retrieval for soil chemical properties. Specifically, the prediction errors for available Fe, Mn, Cu, and Zn were 9.61%, 8.19%, 4.41%, and 11.01%, respectively. This study enables the direct conversion of spectral data into bioavailable HM contents, thereby providing a scientific basis for the rapid and accurate monitoring of soil HM pollution.

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