Use of Hyperspectral Imaging for the Quality Classification of Cooked Turkey Hams

This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900-1700 nm) to evaluate the quality of cooked turkey hams. Different qualities of turkey hams were studied based on their chemical ingredients and processing parameters used during processing. Hyperspectral images were acquired for ham slices originated from each quality class and then their spectral data was extracted. Spectral data was analyzed using Principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. It is seen that from 241 wavelengths, only five wavelengths (980, 1061, 1141, 1215 and 1326 nm) was considered to be the optimum wavelengths for the classification and characterization of turkey hams. The data analysis showed that it is possible to separate different quality turkey hams with few numbers of wavelengths on the basis of their chemical composition. Linear Discriminant Analysis (LDA) showed that the best classification accuracy was 88.57%. The result revealed the potential of NIR hyperspectral imaging as an objective, rapid and non-destructive method for the authentication and classification of cooked turkey ham slices.