Abstract
Based on model population analysis (MPA), the ensemble refinement (ER) has been proposed for outlier detection in calibration transfer. The ER first constructs many subsets of transfer set, and then computes the validation errors of each subset. After that, for each sample, the average error for subsets which include the one sample can be obtained. Finally, the samples with large average errors can be identified as outliers. The simulated dataset has been used to testing this method. The results showed that for calibration transfer methods such as CCA-ICE, DS and SST, ER can all identify outliers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Deng, B., Lu, H., Tan, C., Deng, J., Yin, Y.: Model population analysis in model evaluation. Chemom. Intell. Lab. Syst. 172, 223–228 (2018). https://doi.org/10.1016/j.chemolab.2017.11.016
**e, Y., Hopke, P.K.: Calibration transfer as a data reconstruction problem. Anal. Chim. Acta. 384, 193–205 (1999)
Acknowledgments
The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (31972153), the China Postdoctoral Science Foundation (2019M661758), the Jiangsu Provincial Postdoctoral Science Foundation (2019K014) and the Foundation of Jiangsu University (19JDG010).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Chemical Industry Press
About this paper
Cite this paper
Zheng, K. et al. (2022). Outlier Detection in Calibration Transfer for Near Infrared Spectra. In: Chu, X., Guo, L., Huang, Y., Yuan, H. (eds) Sense the Real Change: Proceedings of the 20th International Conference on Near Infrared Spectroscopy. ICNIR 2021. Springer, Singapore. https://doi.org/10.1007/978-981-19-4884-8_29
Download citation
DOI: https://doi.org/10.1007/978-981-19-4884-8_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4883-1
Online ISBN: 978-981-19-4884-8
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)