Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Abstract

This paper presents a study that was performed for rapid and noninvasive detection of waxed chestnuts using hyper-spectral imaging. A visual near-infrared (400–1026 nm) hyper-spectral imaging system was assembled to acquire scattering images from two groups of chestnuts (waxed and non-waxed chestnuts). The spectra of the samples were extracted from the hyper-spectral images using image segmentation process. Then multiplicative scatter correction (MSC) was conducted to preprocess the original spectra. Effective wavelengths were selected to reduce the computational burden of the hyper-spectral data. Using the seven effective wavelengths that were obtained from a successive projections algorithm (SPA), three calibration algorithms were compared: partial least squares regression (PLSR), multiple linear regression (MLR) and linear discriminant analysis (LDA). The best model for discriminating between waxed and non-waxed chestnuts was found to be the MSC-SPA-MLR model.