TY - JOUR

T1 - Calibration model fusion

AU - Halberg, Helene Fog Froriep

AU - Holst, Anette Yde

AU - Kaufmann, Niels

AU - Bro, Rasmus

N1 - Publisher Copyright: © 2021 John Wiley & Sons, Ltd.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1002/cem.3350

DO - 10.1002/cem.3350

M3 - Journal article

AN - SCOPUS:85105941778

VL - 37

JO - Journal of Chemometrics

JF - Journal of Chemometrics

SN - 0886-9383

IS - 3

M1 - e3350

ER -