Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP) Part 2: Classification of post-consumer plastics by remote NIR spectroscopy using an InGaAs diode array

The supervised working FuzzyARTMAP pattern recognition algorithm has been applied to automated identification of post-consumer plastics by near-infrared spectroscopy (NIRS). Experimentally, a remote operating parallel multisensor device, based on a rapid InGaAs diode array detector combined with new collimation optics, has been used. The laboratory setup allows on-line identification of more than 100 spectra per second. Internal parameter settings of FuzzyARTMAP were varied to explore their influence on the classifier's behavior. Discrimination results obtained were better than those from an optimized multilayer feedforward backpropagation artificial neural network (MLF-BP) and significantly better than those provided by the partial least squares method (PLS2). Additional advantages of FuzzyARTMAP compared to these two classifiers are a significantly higher speed of calibration, the chemical interpretability of network weight coefficients and a built-in detector against extrapolations.