Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features

Hamail Ayaz,Manuel Mazzara,Ahmed Sohaib,Muhammad Ahmad

doi:10.3390/app10217783

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%.

Highlights

Hyperspectral Imaging (HSI) systems are an advanced form of imaging system, which have been used in a plethora of research areas such as remote sensing [1,2], food science, chemistry [3], medical imaging [4] and other raw materials [5]
The combination of spatial and spectral information across the electromagnetic spectrum provides a unique signature for its material, which helps in inspection, authentication, classification, and identification of different materials
This section explains the experiment held for the classification of meat types through the traditional Machine Learning (ML) technique (SVM, K-Nearest Neighbor (KNN)) and state-of-the-art 3D-Deep Learning (DL) model

Summary

Introduction

Hyperspectral Imaging (HSI) systems are an advanced form of imaging system, which have been used in a plethora of research areas such as remote sensing [1,2], food science, chemistry [3], medical imaging [4] and other raw materials [5]. The combination of spatial and spectral information across the electromagnetic spectrum provides a unique signature for its material, which helps in inspection, authentication, classification, and identification of different materials. The HSI system provides the visualization of its material, utilizing an image alongside the distribution of its chemical components. HSI provides information in the form of cubical structure, i.e., 3-dimensional cube (called Spectral cube); HSI : X, Y, λ → R, where [ X, Y ]. Represents spatial coordinates, λ explains the fundamental wavelength of an image and R represents the reflectance of minced meat. This entire electromagnetic spectrum (wavelength (λ)) generates a chemical composition (nature) of the material via the intensity of the reflected light. The HSI system forms a stack of images throughout its entire wavelength, where each image is a gray-scale (monochromic) and commonly represented as a band at each point of wavelength (Bλ ) and defined as

Methods

Results

Conclusion