Near Infrared Hyperspectral Image Regression: On the Use of Prediction Maps as a Tool for Detecting Model Overfitting

Abstract

Calibration models developed from hyperspectral imaging data may be applied at the pixel level to generate prediction maps that estimate the spatial distribution of components in a sample. Such prediction maps facilitate direct visual interpretation of model performance, and performance indicators can be extracted from them. These maps can be used as a tool to evaluate calibration models developed on hyperspectral imaging data. This paper presents a method for calibration model evaluation based on information obtained from prediction maps and demonstrates its usefulness for preventing overfitting. Partial least-squares regression was used for model calibration in this study, although in principle the proposed method may be used to evaluate other multivariate calibration methods, e.g. ridge regression and principal-components regression.