2018-01-232018-01-232013-07-011873-3573http://hdl.handle.net/2133/10470A 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.application/pdfengopenAccessPartial least-squaresMultivariate CalibrationVariable SelectionPre-processing SelectionSample SelectionOutlier DetectionAllegrini, FrancoOlivieri, Alejandro CésarUniversidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y FarmacéuticasElsevier