ABSTRACT Title of Dissertation: HYPERSPECTRAL IMAGING AND PATTERN RECOGNITION TECHNOLOGIES FOR REAL TIME FRUIT SAFETY AND QUALITY INSPECTION

Title of Dissertation: HYPERSPECTRAL IMAGING AND PATTERN RECOGNITION TECHNOLOGIES FOR REAL TIME FRUIT SAFETY AND QUALITY INSPECTION Xuemei Cheng, Doctor of Philosophy, 2004 Dissertation directed by: Professor Yang Tao Department of Biological Resources Engineering Hyperspectral band selection and band combination has become a powerful tool and have gained enormous interest among researchers. An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. An integrated principal component analysis (PCA) and Fisher linear discriminant (FLD) method has been developed for feature band selection, and other pattern recognition technologies have been applied and compared with the developed method. The results on different types of defects from cucumber and apple samples show that the integrated PCA-FLD method outperforms PCA, FLD and canonical discriminant methods when they are used separately for classification. The