Abstract

A spectroscopic near-infrared imaging system, using a focal plane array (FPA) detector, is presented for remote and on-line measurements on a macroscopic scale. On-line spectroscopic imaging requires high-speed sensors and short image processing steps. Therefore, the use of a focal plane array detector in combination with fast chemometric software is investigated. As these new spectroscopic imaging systems generate so much data, multivariate statistical techniques are needed to extract the important information from the multidimensional spectroscopic images. These techniques include principal component analysis (PCA) and linear discriminant analysis (LDA) for supervised classification of spectroscopic image data. Supervised classification is a tedious task in spectroscopic imaging, but a procedure is presented to facilitate this task and to provide more insight into and control over the composition of the datasets. The identification system is constructed, implemented, and tested for a real-world application of plastic identification in municipal solid waste.

Multi-spectral infrared spectroscopy for robust plastic identification

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