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Robust multicomponent IR-to-concentration model regression
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DOI：10.1016/j.ces.2014.04.013.png)
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Abstract
En 中文
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.
Keywords:
Multi-component calibration
ATR-FTIR
Process monitoring
Multi-solute crystallization
Chemometrics
Supervised learning
Journal
IF:
4.3
Papers:
2.2W
Citations:
5.5W