Improving the Model Migration Ability by a Hyperspectral Method With a High Spatial Resolution

In the spectral analysis of a complex solution, the model migration ability is a key challenge. The influence of changes in external factors (measurement environmental changes) is the main reason for the poor model migration ability. If this influence is not well suppressed, it may affect the model migration ability and reduce the robustness of the model. Therefore, effectively suppressing this influence can enhance the model migration ability and improve accuracy. In this paper, we employ the spatial and time domains in addition to the wavelength domain and propose a high-spatial-resolution hyperspectral method that transforms the information of the external factor changes in the spectrum into DC (direct-current) components in the spatial and time domains. Consequently, the information of the external factor changes is separated from the effective information of the tested substance. An algorithm is proposed to extract parameter spectra that are only related to the information of the tested substance. Then, the traditional transmission spectra are replaced by the extracted parameter spectra in the modeling. We design an experiment to spectrally analyze hemoglobin (HB), and two kinds of measurement containers are used to simulate a type of external factor. To verify the effectiveness of the proposed method, we determine whether the model can be migrated to an environment with a different measurement container. The experimental results show that the prediction accuracy for HB and the model migration ability are satisfactory. The results indicate that the high-spatial-resolution hyperspectral method can effectively suppress the influence of external factor changes on spectral analysis and improve the model migration ability.