Detection of soil characteristics based on near-infrared spectroscopy, spectral-characteristics fusion, and error minimization

Near-infrared spectroscopy provides a non-destructive and rapid route for soil analysis. However, conventional chemometric models based on nonlinear supervised learning remain limited for complex samples and multi-characteristics prediction. This paper proposes a chemometric framework based on spectral-characteristics fusion and minimization of representation mapping or prediction error. First, spectral-characteristics fusion integrated spectral data with single or multiple soil characteristics. Subsequently, quantitative models were constructed using a supervised spectral-to-compositional representation mapping model and a dynamic series forecasting model, which minimize the spectral-to-compositional representation mapping error and the prediction error, respectively. The dynamic series forecasting model was constructed based on dynamic sequential data analogous to time series data derived from spectral-characteristics fused data, with sliding windows covering all soil-characteristic positions. Experiments on organic samples from the LUCAS 2009 topsoil data showed that fused data with great continuity facilitated model construction, and the proposed framework achieved higher predictive accuracy than the selected conventional supervised learning baselines. This paper provides a near-infrared spectral-characteristics fusion and error-minimized prediction strategy for compositional analysis of complex samples, with potential applicability beyond soil analysis.