Abstract Inter-instrument variability and measurement condition changes pose great challenges for the maintenance of terahertz calibration models. The predictive accuracy of the trained calibration models is questionable for newly measured data sets. Obviously, this problem may hinder practical applications of terahertz spectroscopy. To tackle this problem, for the first time we explore to improve the transfer ability of calibration model for terahertz spectroscopy via spectral space transformation. This method tries to minimize the spectral inconsistency induced by the changes of instruments or measurement conditions via spectral transformation between two spectral spaces spanned by the spectra of pure chemical transfer samples. Furthermore, multi-way partial least squares methods were carried out and compared with this approach based on a terahertz spectra dataset. Experiments show that spectral space transformation outperforms other methods in the sense of prediction accuracy and applicability; with this method the performance of the master model applied to slave dataset is consistently enhanced, which proves their efficacy and usability in real-life applications.

中文翻译：

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提高太赫兹光谱校准模型的传递能力

摘要 仪器间的可变性和测量条件的变化对太赫兹校准模型的维护提出了巨大的挑战。对于新测量的数据集，经过训练的校准模型的预测准确性值得怀疑。显然，这个问题可能会阻碍太赫兹光谱的实际应用。为了解决这个问题，我们首次探索了通过光谱空间变换来提高太赫兹光谱校准模型的传递能力。该方法试图通过纯化学转移样品光谱跨越的两个光谱空间之间的光谱转换，尽量减少仪器或测量条件变化引起的光谱不一致性。此外，进行了多路偏最小二乘法，并与基于太赫兹光谱数据集的这种方法进行了比较。实验表明，谱空间变换在预测精度和适用性方面优于其他方法；通过这种方法，应用于从数据集的主模型的性能不断增强，这证明了它们在实际应用中的有效性和可用性。