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Spectral imaging becomes more and more interesting not only for agricultural use but also for industrial application. Especially wavelength in the near infrared (NIR) range can be used for materials classification. Today sorting systems for plastics are available in different variations, utilizing single-point spectroscopy and the different characteristics of plastics in the SWIR band. Sorting systems for paper and cardboard will have increased significance because better sorting can increase the price of the secondary material and reduce the need of chemicals in paper production. However, sorting paper qualities is a very difficult task due to the close similarities between the materials. The present work describes the development of an unique industrial inline material sorting system using spectral imaging technique focusing on classification for cellulose based materials such as pulp, paper and cardboard. It deals with the hardware requirements for industrial use of spectral imaging solutions as well as with adjustment and calibration techniques. Due to needed classification speed the software design and classification methods are described under this focus. To cope with the vast amount of spectral data and to implement a stable and reliable classification algorithm for different materials chemometric standard methods are used. The PCA is used to reduce data and obtain as much information of the samples's characteristics as possible by transforming the original multidimensional data-space into a space with lower dimensions. However PCA is no method to discriminate between classes, it allows to separate cellulose-based materials from plastics. For further discrimination an LDA-Algorithm is used. All chemometric methods need training data sets of well defined samples. To classify an unknown spectra, it is necessary to create models for the classes to be distinguished from each other inside the transformed data-space. Training spectra have to be carefully selected to represent the characteristics of a specific class best possible. The classification-tree uses an adapted KNN-algorithm. In order to avoid a serious bottleneck in processing-speed the continuous result space was converted into discrete space representation.
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