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- Title
Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features.
- Authors
Ayaz, Hamail; Ahmad, Muhammad; Mazzara, Manuel; Sohaib, Ahmed
- Abstract
Minced meat substitution is one of the most common forms of food fraud in the meat industry. Recently, Hyperspectral Imaging (HSI) has been used for the classification and identification of minced meat types. However, conventional methods are based only on spectral information and ignore the spatial variability of the data. Moreover, these methods first tend to reduce the size of the data, which to some extent ignores the abstract level information and does not preserve the spatial information. Therefore, this work proposes a novel Isos-bestic wavelength reduction method for the different minced meat types, by retaining only Myoglobin pigments (Mb) in the meat spectra. A total of 60 HSI cubes are acquired using Fx 10 Hyperspectral sensor. For each HSI cube, a set of preprocessing schemes is applied to extract the Region of Interest (ROI) and spectral preprocessing, i.e., Golay filtering. Later, these preprocessed HSI cubes are fed into a 3D-Convolutional Neural Network (3D-CNN) model for nonlinear feature extraction and classification. The proposed pipeline outperformed several state-of-the-art methods, with an overall accuracy of 94.0 % .
- Subjects
MEAT industry; FEATURE extraction; MYOGLOBIN; CLASSIFICATION; SPECTRAL imaging; COLOR of meat
- Publication
Applied Sciences (2076-3417), 2020, Vol 10, Issue 21, p7783
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app10217783