Calibration model fusion

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Submitted manuscript, 371 KB, PDF document

Calibration model maintenance is often overlooked but is a significant part of successful use of multivariate calibration models, for example, in process monitoring and optimization. In some cases, companies are maintaining tens or even hundreds of calibration models. This could be partial least squares (PLS) calibration models pertaining to different recipes or raw materials or neural network based models covering different production sites. Maintaining such a high number of models is cumbersome and expensive. Sometimes, a solution presented for this problem is to merge all the models into one, but this often comes at the expense of significantly higher prediction errors. In this paper, we suggest a new approach for rationally merging calibration models in order to optimally balance the prediction error and maintenance workload. We do this by systematically merging models that lower the error as much as possible and hence provide a sort of optimal clustering or fusion of calibration models. We showcase the new approach on a case based on infrared spectroscopy applied to dairy production.

Original languageEnglish
Article numbere3350
JournalJournal of Chemometrics
Issue number3
Number of pages8
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 272061892