Improved Calibration of Near-Infrared Spectra by Using Ensembles of Neural Network Models

TL;DR

This paper introduces an ensemble of neural network models for near-infrared spectroscopy calibration, significantly improving accuracy and robustness over traditional linear regression methods.

Contribution

It proposes a novel ensemble approach using resampled neural networks, enhancing calibration performance in NIR spectroscopy.

Findings

Ensemble models outperform single neural networks in calibration accuracy.

The approach yields more robust models against data variability.

Significant improvement over conventional linear regression methods.

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.