An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration.
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.
Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química de Rosario (IQUIR-CONICET), Suipacha 531, Rosario S2002LRK, Argentina.
Franco
F
Allegrini
Alejandro C
AC
Olivieri
D000465:Algorithms; D002138:Calibration; D016018:Least-Squares Analysis; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D012984:Software; D013057:Spectrum Analysis / Q000592:standards / Q000706:statistics & numerical data*
Multivariate calibration; Outlier detection; Partial least-squares; Pre-processing selection; Sample selection; Variable selection