Infrared absorbance measurements can be made in situ and rapidly. Calibrating these measurements to give solution compositions can therefore yield a powerful tool for process monitoring and control. In many applications it is desirable to monitor the concentrations of multiple components in a complex solution under varying process conditions (which may introduce error in the absorbance measurements). Establishing a model that is capable of accurately predicting the concentrations of multiple components from infrared absorbance measurements that may be corrupted by error requires a carefully designed calibration procedure-a key part of which is model regression. In this article, a number of commonly used multivariate regression techniques are examined in the context of developing a model for simultaneously predicting the concentrations of four solutes from noisy infrared absorbance measurements. In addition, a tailored support vector regression algorithm-designed to produce a robust (measurement error-insensitive) calibration model-is developed, tested, and compared against these established regression algorithms. (C) 2014 Elsevier Ltd. All rights reserved.