Improving the transfer ability of calibration model for terahertz spectroscopy

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.

Keywords:

• Calibration model maintenance
• partial least squares
• spectral space transformation
• spectral standardization
• terahertz spectra

Acknowledgments

We gratefully thank Shihan Yan from Chongqing Institute of Green and Intelligent Technology for experimental support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under [Grant number 31771670]; Fundamental Research Funds for the Central Universities under [Grant number XDJK2020C029]; and Chongqing Overseas Returnees innovation and Entrepreneurship Project under [Grant number cx2018101].