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

作者：Allegrini Franco; Olivieri Alejandro C^*

来源：Talanta, 2013, 115: 755-760.

DOI：10.1016/j.talanta.2013.06.051

• Multivariate calibration
• Variable selection
• Pre-processing selection
• Sample selection
• Outlier detection
• Partial least-squares

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.

• 出版日期2013-10-15