A heuristic and parallel simulated annealing algorithm for variable selection in near-infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near-infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high-quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near-infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure-activity relationship problems. A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near-infrared spectroscopy (NIR) analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high quality candidate solutions and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several NIR data sets have been used to test the algorithm. Results shown that improved robust and predictive regression models were obtained by the new algorithm.