1. Improved Calibration of Near-Infrared Spectra by Using Ensembles of Neural Network Models
- Author
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Ukil, A., Bernasconi, J., Braendle, H., Buijs, H., and Bonenfant, S.
- Subjects
Computer Science - Neural and Evolutionary Computing - Abstract
IR or near-infrared (NIR) spectroscopy is a method used to identify a compound or to analyze the composition of a material. Calibration of NIR spectra refers to the use of the spectra as multivariate descriptors to predict concentrations of the constituents. To build a calibration model, state-of-the-art software predominantly uses linear regression techniques. For nonlinear calibration problems, neural network-based models have proved to be an interesting alternative. In this paper, we propose a novel extension of the conventional neural network-based approach, the use of an ensemble of neural network models. The individual neural networks are obtained by resampling the available training data with bootstrapping or cross-validation techniques. The results obtained for a realistic calibration example show that the ensemble-based approach produces a significantly more accurate and robust calibration model than conventional regression methods., Comment: 7 pages
- Published
- 2015
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