Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, Anhui 230031, People's Republic of China; University of Science and Technology of China, No. 96 Jinzhai Road, Hefei, Anhui 230026, People's Republic of China.
Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, Anhui 230031, People's Republic of China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Apr 5;230:118053. doi: 10.1016/j.saa.2020.118053. Epub 2020 Jan 11.
Considering that the spectral signals vary among different instruments, calibration transfer is required for further popularization and application of the near-infrared spectroscopy (NIRS). To achieve good calibration transfer results, spectral variables with stable and consistent signals between instruments and containing the target component information should be selected. In this study, a correlation-analysis-based wavelength selection method (CAWS) is proposed for calibration transfer. This method relies on the selection of wavelengths at which the spectral responses of master and slave instruments are well correlated (high absolute values of Pearson's correlation coefficient (|R|)). The proposed CAWS method was applied to two available datasets, corn and rice bran, and its calibration transfer performances were compared with other wavelength selection methods. The effects of pretreatment methods and calibration transfer algorithms were also assessed. The CAWS optimized models obtained lower root mean square errors of prediction (RMSEP) after calibration transfer, suggesting that the proposed method is capable of effectively improving the efficiency of calibration transfer. Combinations of this method with other wavelength selection methods and calibration transfer algorithms may further enhance the efficiency of calibration transfer, and thus should be thoroughly investigated.
考虑到不同仪器的光谱信号存在差异，近红外光谱（NIRS）需要进行校准传递才能进一步推广和应用。为了获得良好的校准传递结果，应该选择在仪器之间具有稳定且一致信号并且包含目标成分信息的光谱变量。在这项研究中，提出了一种基于相关分析的波长选择方法（CAWS）用于校准传递。该方法依赖于选择主仪器和从仪器的光谱响应具有良好相关性（皮尔逊相关系数绝对值较高（|R|））的波长。将所提出的 CAWS 方法应用于两个可用数据集，即玉米和米糠，并将其校准传递性能与其他波长选择方法进行了比较。还评估了预处理方法和校准传递算法的效果。经过校准传递后，CAWS 优化模型获得了更低的预测均方根误差（RMSEP），这表明该方法能够有效地提高校准传递的效率。将这种方法与其他波长选择方法和校准传递算法相结合，可能会进一步提高校准传递的效率，因此应该进行深入研究。
