Swarm Intelligence for Chemometrics

Abstract

Together with the growing use of advanced analytical instruments [ranging from separation techniques such as gas chromatography to light-based techniques, e.g. near infrared (NIR) spectroscopy], interest in the use of multivariate calibration [e.g. partial least squares (PLS)] has emerged in many different industries (food, chemical, pharmaceutical etc). Although many different modelling techniques can be used, these techniques normally require either a variable selection before model building (e.g. multiple linear regression) or they use a dimension reduction method to create latent variables (e.g. partial least squares regression). In spite of the fact that there are many different variable selection techniques, most attention is paid to those based on swarm intelligence methodologies. The reason is that such methodologies are usually simulated based on animal and insect life behaviour to, for example, find the shortest path between a food source and a nest. From an optimisation point of view, a solution or decision is made by a crowd (e.g. a population of ants) which leads to a more robust and global optimised system (in this case a better subset selection of most relevant variables e.g. wavelengths from NIR spectra).