An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
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Elsevier
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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.