Imperial College London
Portrait Picture

ProfessorRuthMisener

Faculty of Engineering, Department of Computing

Professor in Computational Optimisation
 
 
 
//

Contact

 

+44 (0)20 7594 8315r.misener Website CV

 
 
//

Location

 

379Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Kappatou:2023:10.1021/acs.iecr.2c04583,
author = {Kappatou, C and Odgers, J and García-Muñoz, S and Misener, R},
doi = {10.1021/acs.iecr.2c04583},
journal = {Industrial and Engineering Chemistry Research},
pages = {6196--6213},
title = {An optimization approach coupling pre-processing with model regression for enhanced chemometrics},
url = {http://dx.doi.org/10.1021/acs.iecr.2c04583},
volume = {62},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models.
AU  - Kappatou,C
AU  - Odgers,J
AU  - García-Muñoz,S
AU  - Misener,R
DO  - 10.1021/acs.iecr.2c04583
EP  - 6213
PY  - 2023///
SN  - 0888-5885
SP  - 6196
TI  - An optimization approach coupling pre-processing with model regression for enhanced chemometrics
T2  - Industrial and Engineering Chemistry Research
UR  - http://dx.doi.org/10.1021/acs.iecr.2c04583
UR  - https://pubs.acs.org/doi/10.1021/acs.iecr.2c04583
UR  - http://hdl.handle.net/10044/1/103684
VL  - 62
ER  -
Download