| dc.creator | Allegrini, Franco | |
| dc.creator | Olivieri, Alejandro César | |
| dc.date | 2018-01-23T14:35:26Z | |
| dc.date | 2018-01-23T14:35:26Z | |
| dc.date | 2013-07-01 | |
| dc.date | 2018-01-23T14:35:26Z | |
| dc.date | 2018-01-23T14:35:26Z | |
| dc.date | 2013-07-01 | |
| dc.date.accessioned | 2019-05-17T20:24:12Z | |
| dc.date.available | 2019-05-17T20:24:12Z | |
| dc.identifier | 1873-3573 | |
| dc.identifier | http://hdl.handle.net/2133/10470 | |
| dc.identifier | http://hdl.handle.net/2133/10470 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/2679989 | |
| dc.description | A new optimization strategy for multivariate partial-least-squares (PLS) regression
analysis is described. It was achieved by integrating three efficient strategies to improve
PLS calibration models: (1) variable selection based on ant colony optimization, (2)
mathematical pre-processing selection by a genetic algorithm, and (3) sample selection
through a distance-based procedure. Outlier detection has also been included as part of the model optimization. All the above procedures have been combined into a single algorithm, whose aim is to find the best PLS calibration model within a Monte Carlo-type philosophy. Simulated and experimental examples are employed to illustrate the success of the proposed approach. | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Elsevier | |
| dc.relation | https://www.sciencedirect.com/science/article/pii/S0039914013005511?via%3Dihub | |
| dc.relation | https://dx.doi.org/10.1016/j.talanta.2013.06.051 | |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | |
| dc.rights | Allegrini, Franco | |
| dc.rights | Olivieri, Alejandro César | |
| dc.rights | Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas | |
| dc.rights | Elsevier | |
| dc.rights | openAccess | |
| dc.subject | Partial least-squares | |
| dc.subject | Multivariate Calibration | |
| dc.subject | Variable Selection | |
| dc.subject | Pre-processing Selection | |
| dc.subject | Sample Selection | |
| dc.subject | Outlier Detection | |
| dc.title | An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration | |
