2026-04-17T10:26:15Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/53512022-08-14T22:42:07Zcom_10259_4249com_10259_5086com_10259_2604col_10259_4250
Repositorio Institucional de la Universidad de Burgos
author
Ruiz Miguel, Santiago
author
Sarabia Peinador, Luis Antonio
author
Ortiz Fernández, Mª Cruz
author
Sánchez Pastor, Mª Sagrario
2020-06-23T13:42:14Z
2020-06-23T13:42:14Z
2020-08
0169-7439
http://hdl.handle.net/10259/5351
10.1016/j.chemolab.2020.104040
The paper contains a discussion about the null spaces associated to linear prediction models for the particular case of Partial Least Squares regression models. The discussion separately considers the two existing null spaces: the one in the input space related to the projection onto the latent space and the null space, coming from the projection space, corresponding to the mapping of the scores onto the predicted responses.
The paper also explores the impact of such null spaces in the definition of the design space around some feasible solutions obtained by inverting the prediction model, via several cases with simulated and real data from the literature. The case-studies serve to illustrate the discussion and the need of considering points in the two null spaces, rather than just take into account the null space within the latent space.
They also serve to show how to address the use of the resulting vectors in the design space to maintain the desired quality by modifying the tunable (maneuverable) process variables to compensate for variations due to some other feature variables not so easy to control.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Partial least squares
Process analytical technology
Quality by design
Linear application
Null space
Model inversion
Residual spaces in latent variables model inversion and their impact in the design space for given quality characteristics
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