Comparison of an adaptive resonance theory based neural network (ART-2a) against other classifiers for rapid sorting of post consumer plastics by remote near-infrared spectroscopic sensing using an In

Author(s): Buydens, L. | Cammann, K. | Feldhoff, R. | Kantimm, T. | Melssen, W. | Quick, L. | Wienke, D. | Winter, F. | van der Broek, W. | HuthFehre, T. |

Year: 1995

Citation: Analytica Chimica Acta, Volume 317, 1-16

Abstract: An Adaptive Resonance Theory Based Artificial Neural Network (ART-2a) has been compared with Multilayer Feedforward Backpropagation of Error Neural Networks (MLF-BP) and with the SIMCA classifier. All three classifiers were applied to achieve rapid sorting of post-consumer plastics by remote near-infrared (NIR) spectroscopy. A new semiconductor diode array detector based on InGaAs technology has been experimentally tested for measuring the NIR spectra. It has been found by a cross validation scheme that MLF-BP networks show a slightly better discrimination power than ART-2a networks. Both types of artificial neural networks perform significantly better than the SIMCA method. A median sorting purity of better than 98% can be guaranteed for non-black plastics. More than 75 samples per second can be identified by the combination InGaAs diode array/neural network. However, MLF-BP neural networks can definitely not extrapolate. Uninterpretable predictions were observed in case of test samples that truly belong to a particular class but that are located outside the subspace defined by training set.

Topics: Machine Learning, Applications: Chemical Analysis, Industrial Control, Models: ART 2-A,

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