Article,
Plastic material identification with spectroscopic near infrared imaging and artificial neural networks
Analytica Chimica Acta, 361 (1-2): 161--176 (1998)Interessant voor ScanPoint en plastic scheidingsproject.
DOI: DOI: 10.1016/S0003-2670(98)00012-9
Abstract
A remote sensing spectroscopic near infrared (NIR) system has been installed in an experimental laboratory setup for real-time plastic identification in mixed household waste. The identification of waste objects is performed in two steps. First, the experimental measurement setup is used for the acquisition of the spectroscopic image data and second, a non-linear transformation is performed by a neural network for supervised classification of these measured images. This new identification system needs an evaluation of its on-line classification performance. However, not only the percentage of correct material classification is of interest, but also the corresponding precision and the circumstances of operation (robustness) such as differences in temperature and humidity. Furthermore, this qualitative identification system incorporates additional complications with respect to the variability of the sensor response, such as variable waste sample positions and shapes accompanied with contaminated waste samples. In order to validate such a system, the robustness, repeatability and reproducibility of the classification system are considered. The final identification system is able to identify plastics with the required success rate of 80%, but improvement is to be expected when some experimental parameters can be stabilized.
