Classification and Prediction Methods

Quantification or classification is a key objective of hyperspectral chemical imaging (HCI). In this chapter, steps involved in the development of classification and prediction models from HCI data are explained, from initial data pretreatment, through to calibration set development and construction of prediction maps using partial least squares discriminant analysis (PLS-DA). As a case study, we demonstrate the prediction of seven classes in an example near infrared (NIR) HCI image containing objects of different chemical composition. The varied effects of spectral pretreatments, sampling procedures, threshold estimation methods and image post-processing are compared in terms of classification model performance and via visual interpretation of prediction maps. The use of graphical data representations such as histograms and color coded prediction maps is presented for further understanding and aiding the development of multivariate model classification models.