T1 Residual spaces in latent variables model inversion and their impact in the design space for given quality characteristics
A1 Ruiz Miguel, Santiago
A1 Sarabia Peinador, Luis Antonio
A1 Ortiz Fernández, Mª Cruz
A1 Sánchez Pastor, Mª Sagrario
K1 Partial least squares
K1 Process analytical technology
K1 Quality by design
K1 Linear application
K1 Null space
K1 Model inversion
K1 Química analítica
K1 Chemistry, Analytic
AB 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.
PB Elsevier
SN 0169-7439
YR 2020
FD 2020-08
LK http://hdl.handle.net/10259/5351
UL http://hdl.handle.net/10259/5351
LA eng
NO European Regional Development Fund (FEDER) through Spanish Agencia Estatal de Investigación (project CTQ2017-88894-R)
DS Repositorio Institucional de la Universidad de Burgos
RD 22-dic-2024